{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "b0b0f7d0-ec48-41d1-aef6-3b3aa0221398",
   "metadata": {},
   "source": [
    "(lecture_11)=\n",
    "# Ordered Categories\n",
    ":::{post} Jan 7, 2024\n",
    ":tags: statistical rethinking, bayesian inference, ordered categories\n",
    ":category: intermediate\n",
    ":author: Dustin Stansbury\n",
    ":::\n",
    "\n",
    "This notebook is part of the PyMC port of the [Statistical Rethinking 2023](https://github.com/rmcelreath/stat_rethinking_2023) lecture series by Richard McElreath.\n",
    "\n",
    "[Video - Lecture 11 - Ordered Categories](https://youtu.be/VVQaIkom5D0)# [Lecture 11 - Ordered Categories](https://youtu.be/VVQaIkom5D0?si=FkFkWJkDHV6-R04N)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "ffc5506e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Ignore warnings\n",
    "import warnings\n",
    "\n",
    "import arviz as az\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import pymc as pm\n",
    "import statsmodels.formula.api as smf\n",
    "import utils as utils\n",
    "import xarray as xr\n",
    "\n",
    "from matplotlib import pyplot as plt\n",
    "from matplotlib import style\n",
    "from scipy import stats as stats\n",
    "\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "# Set matplotlib style\n",
    "STYLE = \"statistical-rethinking-2023.mplstyle\"\n",
    "style.use(STYLE)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f2b90fde-2228-40ef-ae1d-a4ff3e819195",
   "metadata": {},
   "source": [
    "# Solving Scientific Problems\n",
    "\n",
    "## When solving problems on the edge of knowledge\n",
    "\n",
    "- there's generally not an algorithm available to tell you how to solve it\n",
    "- best we can do is see lots of examples\n",
    "- derive a set of general heuristics for how to attack problems\n",
    "  - try solving a more accessible problem or subproblem\n",
    "  - e.g. try building a simpler model first, then add complexity\n",
    "  - ALWAYS CHECK YOUR WORK -- e.g. via simulation\n",
    "\n",
    "\n",
    "### You Don't always get what you want\n",
    "- May not be able to estimate the desired estimand\n",
    "- You'll likely have to compromise\n",
    "  - e.g. estimating the total effect vs the direct effect\n",
    "  - sensitivity analysis\n",
    "  - estimate counterfactuals"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "75c389cc",
   "metadata": {},
   "source": [
    "# Ethics & Trolley Problem Studies\n",
    "\n",
    "## Principles studied\n",
    "\n",
    "Researchers have tried to catalog trolly problem scenarios along multiple feature dimensions, three common features are:\n",
    "\n",
    "- **Action**: taking an action is less morally permissible than not taking an action (you intervening on a scene is condidered worse than letting the scene play out)\n",
    "- **Intention**: does the actor's direct goal affect the scenario (e.g. intentially killing one person to save five)\n",
    "- **Contact**: intentions are worse if the actor comes into direct contact with the object of the action (e.g. directly pushing a person off a bridge to save others)\n",
    "\n",
    "\n",
    "## Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "8e6165b8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>case</th>\n",
       "      <th>response</th>\n",
       "      <th>order</th>\n",
       "      <th>id</th>\n",
       "      <th>age</th>\n",
       "      <th>male</th>\n",
       "      <th>edu</th>\n",
       "      <th>action</th>\n",
       "      <th>intention</th>\n",
       "      <th>contact</th>\n",
       "      <th>story</th>\n",
       "      <th>action2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>cfaqu</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>96;434</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>Middle School</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>aqu</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>cfbur</td>\n",
       "      <td>3</td>\n",
       "      <td>31</td>\n",
       "      <td>96;434</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>Middle School</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>bur</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>cfrub</td>\n",
       "      <td>4</td>\n",
       "      <td>16</td>\n",
       "      <td>96;434</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>Middle School</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>rub</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>cibox</td>\n",
       "      <td>3</td>\n",
       "      <td>32</td>\n",
       "      <td>96;434</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>Middle School</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>box</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>cibur</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>96;434</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>Middle School</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>bur</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    case  response  order      id  age  male            edu  action  \\\n",
       "0  cfaqu         4      2  96;434   14     0  Middle School       0   \n",
       "1  cfbur         3     31  96;434   14     0  Middle School       0   \n",
       "2  cfrub         4     16  96;434   14     0  Middle School       0   \n",
       "3  cibox         3     32  96;434   14     0  Middle School       0   \n",
       "4  cibur         3      4  96;434   14     0  Middle School       0   \n",
       "\n",
       "   intention  contact story  action2  \n",
       "0          0        1   aqu        1  \n",
       "1          0        1   bur        1  \n",
       "2          0        1   rub        1  \n",
       "3          1        1   box        1  \n",
       "4          1        1   bur        1  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "TROLLEY = utils.load_data(\"Trolley\")\n",
    "N_TROLLEY_RESPONSES = len(TROLLEY)\n",
    "N_RESPONSE_CATEGORIES = TROLLEY.response.max()\n",
    "TROLLEY.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ac830d4b-70bc-43dc-9aed-93badb08f26e",
   "metadata": {},
   "source": [
    "- 9330 total responses\n",
    "- 331 individuals\n",
    "- 30 different trolley problem scenarios\n",
    "- vary along action, intention, contact dimensions\n",
    "- responses are ordered integer values ranging from 1 to 7 indicating the \"appropriateness\" of action\n",
    "  - not counts\n",
    "  - ordered but not continous ordered\n",
    "  - bounded"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "81ac60ad",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 720x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "response_counts = TROLLEY.groupby(\"response\").count()[\"action\"]\n",
    "bars = plt.bar(response_counts.index, response_counts.values, width=0.25)\n",
    "plt.xlabel(\"outcome\")\n",
    "plt.ylabel(\"frequency\")\n",
    "plt.title(f\"Ordered Categorical ({N_TROLLEY_RESPONSES:,} Responses)\");"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cc47ecd0",
   "metadata": {},
   "source": [
    "1) Estimand\n",
    "How do <span style=\"color:blue\">**Action, intention, and contact**</span> influence <span style=\"color:red\">**response**</span>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "9d529b8b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/svg+xml": [
       "<?xml version=\"1.0\" encoding=\"UTF-8\" standalone=\"no\"?>\n",
       "<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n",
       " \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\n",
       "<!-- Generated by graphviz version 12.0.0 (20240803.0821)\n",
       " -->\n",
       "<!-- Pages: 1 -->\n",
       "<svg width=\"610pt\" height=\"231pt\"\n",
       " viewBox=\"0.00 0.00 610.33 230.72\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n",
       "<g id=\"graph0\" class=\"graph\" transform=\"scale(1 1) rotate(0) translate(4 226.72)\">\n",
       "<polygon fill=\"white\" stroke=\"none\" points=\"-4,4 -4,-226.72 606.33,-226.72 606.33,4 -4,4\"/>\n",
       "<!-- X -->\n",
       "<g id=\"node1\" class=\"node\">\n",
       "<title>X</title>\n",
       "<ellipse fill=\"none\" stroke=\"blue\" cx=\"291.09\" cy=\"-203.28\" rx=\"145.49\" ry=\"19.45\"/>\n",
       "<text text-anchor=\"middle\" x=\"291.09\" y=\"-199.73\" font-family=\"Times,serif\" font-size=\"14.00\">action, intenition, contact, X</text>\n",
       "</g>\n",
       "<!-- R -->\n",
       "<g id=\"node2\" class=\"node\">\n",
       "<title>R</title>\n",
       "<ellipse fill=\"none\" stroke=\"red\" cx=\"537.45\" cy=\"-99.28\" rx=\"64.88\" ry=\"19.45\"/>\n",
       "<text text-anchor=\"middle\" x=\"537.45\" y=\"-95.73\" font-family=\"Times,serif\" font-size=\"14.00\">response, R</text>\n",
       "</g>\n",
       "<!-- X&#45;&gt;R -->\n",
       "<g id=\"edge1\" class=\"edge\">\n",
       "<title>X&#45;&gt;R</title>\n",
       "<path fill=\"none\" stroke=\"red\" d=\"M393,-189.05C407.95,-185.23 422.92,-180.4 436.58,-174.28 463.4,-162.27 489.82,-141.95 508.74,-125.49\"/>\n",
       "<polygon fill=\"red\" stroke=\"red\" points=\"510.87,-128.28 516.01,-119.02 506.21,-123.06 510.87,-128.28\"/>\n",
       "</g>\n",
       "<!-- S -->\n",
       "<g id=\"node3\" class=\"node\">\n",
       "<title>S</title>\n",
       "<ellipse fill=\"none\" stroke=\"black\" cx=\"291.09\" cy=\"-146.28\" rx=\"45.25\" ry=\"19.45\"/>\n",
       "<text text-anchor=\"middle\" x=\"291.09\" y=\"-142.73\" font-family=\"Times,serif\" font-size=\"14.00\">story, S</text>\n",
       "</g>\n",
       "<!-- S&#45;&gt;R -->\n",
       "<g id=\"edge2\" class=\"edge\">\n",
       "<title>S&#45;&gt;R</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M332.99,-138.41C370.61,-131.18 427.05,-120.32 470.95,-111.88\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"471.46,-115.34 480.62,-110.02 470.14,-108.47 471.46,-115.34\"/>\n",
       "</g>\n",
       "<!-- E -->\n",
       "<g id=\"node4\" class=\"node\">\n",
       "<title>E</title>\n",
       "<ellipse fill=\"none\" stroke=\"black\" cx=\"291.09\" cy=\"-52.28\" rx=\"70.18\" ry=\"19.45\"/>\n",
       "<text text-anchor=\"middle\" x=\"291.09\" y=\"-48.73\" font-family=\"Times,serif\" font-size=\"14.00\">education, E</text>\n",
       "</g>\n",
       "<!-- E&#45;&gt;R -->\n",
       "<g id=\"edge3\" class=\"edge\">\n",
       "<title>E&#45;&gt;R</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M349.69,-63.36C386.16,-70.37 433.41,-79.46 471.26,-86.74\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"470.2,-90.1 480.68,-88.55 471.53,-83.23 470.2,-90.1\"/>\n",
       "</g>\n",
       "<!-- Y -->\n",
       "<g id=\"node5\" class=\"node\">\n",
       "<title>Y</title>\n",
       "<ellipse fill=\"none\" stroke=\"black\" cx=\"54.8\" cy=\"-89.28\" rx=\"39.42\" ry=\"19.45\"/>\n",
       "<text text-anchor=\"middle\" x=\"54.8\" y=\"-85.73\" font-family=\"Times,serif\" font-size=\"14.00\">age, Y</text>\n",
       "</g>\n",
       "<!-- Y&#45;&gt;R -->\n",
       "<g id=\"edge4\" class=\"edge\">\n",
       "<title>Y&#45;&gt;R</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M94.32,-90.08C173.77,-91.73 357.05,-95.55 460.97,-97.71\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"460.73,-101.2 470.8,-97.91 460.87,-94.21 460.73,-101.2\"/>\n",
       "</g>\n",
       "<!-- Y&#45;&gt;E -->\n",
       "<g id=\"edge6\" class=\"edge\">\n",
       "<title>Y&#45;&gt;E</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M92.92,-83.42C126.49,-78.12 176.85,-70.16 218.27,-63.62\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"218.59,-67.11 227.92,-62.1 217.5,-60.2 218.59,-67.11\"/>\n",
       "</g>\n",
       "<!-- G -->\n",
       "<g id=\"node6\" class=\"node\">\n",
       "<title>G</title>\n",
       "<ellipse fill=\"none\" stroke=\"black\" cx=\"54.8\" cy=\"-23.28\" rx=\"54.8\" ry=\"19.45\"/>\n",
       "<text text-anchor=\"middle\" x=\"54.8\" y=\"-19.73\" font-family=\"Times,serif\" font-size=\"14.00\">gender, G</text>\n",
       "</g>\n",
       "<!-- G&#45;&gt;R -->\n",
       "<g id=\"edge5\" class=\"edge\">\n",
       "<title>G&#45;&gt;R</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M103.65,-14C177.37,-1.76 322.18,14.06 436.58,-24.28 464.98,-33.8 491.8,-54.88 510.49,-72.27\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"508.01,-74.74 517.64,-79.15 512.86,-69.69 508.01,-74.74\"/>\n",
       "</g>\n",
       "<!-- G&#45;&gt;E -->\n",
       "<g id=\"edge7\" class=\"edge\">\n",
       "<title>G&#45;&gt;E</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M106.93,-29.61C138.64,-33.53 179.93,-38.64 215.28,-43.02\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"214.67,-46.47 225.03,-44.23 215.53,-39.52 214.67,-46.47\"/>\n",
       "</g>\n",
       "</g>\n",
       "</svg>\n"
      ],
      "text/plain": [
       "<graphviz.graphs.Digraph at 0x7f33fdd6c3b0>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "utils.draw_causal_graph(\n",
    "    edge_list=[(\"X\", \"R\"), (\"S\", \"R\"), (\"E\", \"R\"), (\"Y\", \"R\"), (\"G\", \"R\"), (\"Y\", \"E\"), (\"G\", \"E\")],\n",
    "    node_props={\n",
    "        \"X\": {\"color\": \"blue\", \"label\": \"action, intenition, contact, X\"},\n",
    "        \"R\": {\"color\": \"red\", \"label\": \"response, R\"},\n",
    "        \"S\": {\"label\": \"story, S\"},\n",
    "        \"E\": {\"label\": \"education, E\"},\n",
    "        \"Y\": {\"label\": \"age, Y\"},\n",
    "        \"G\": {\"label\": \"gender, G\"},\n",
    "    },\n",
    "    edge_props={\n",
    "        (\"X\", \"R\"): {\"color\": \"red\"},\n",
    "    },\n",
    "    graph_direction=\"LR\",\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "892859d2",
   "metadata": {},
   "source": [
    "## Ordered Categories\n",
    "\n",
    "- Discrete categories\n",
    "- Categories have an order, thus 7 > 6 > 5, etc., but 7 not necessarily 7x that of 1\n",
    "- Distance between each values is not constant, and unclear\n",
    "- Anchor points (e.g. 4 here is \"meh\")\n",
    "  - Different people have different anchor points"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5da8f360",
   "metadata": {},
   "source": [
    "## Ordered = Cumulative\n",
    "\n",
    "- rather than $p(x=5)$, $p(x<=5)$\n",
    "  \n",
    "### Cumulative Distribution Function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "ce5ccdd3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(1, 2, figsize=(10, 5))\n",
    "xticks = range(1, 8)\n",
    "\n",
    "cumulative_counts = response_counts.cumsum()\n",
    "\n",
    "plt.sca(axs[0])\n",
    "plt.bar(response_counts.index, response_counts.values, color=\"C0\", width=0.25)\n",
    "plt.xlabel(\"outcome\")\n",
    "plt.xticks(xticks)\n",
    "plt.ylabel(\"frequency\")\n",
    "plt.ylim([0, 10_000])\n",
    "\n",
    "# Cumulative frequency\n",
    "plt.sca(axs[1])\n",
    "plt.bar(response_counts.index, cumulative_counts.values, color=\"k\", width=0.25)\n",
    "offsets = cumulative_counts.values[1:] - response_counts.values[1:]\n",
    "plt.bar(\n",
    "    cumulative_counts.index[1:], response_counts.values[1:], bottom=offsets, color=\"C0\", width=0.25\n",
    ")\n",
    "\n",
    "for ii, y in enumerate(offsets):\n",
    "    x0 = ii + 1\n",
    "    x1 = x0 + 1\n",
    "    plt.plot((x0, x1), (y, y), color=\"C0\", linestyle=\"--\")\n",
    "\n",
    "plt.xlabel(\"outcome\")\n",
    "plt.xticks(xticks)\n",
    "plt.ylabel(\"cumulative frequency\")\n",
    "plt.ylim([0, 10_000]);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "84d5cb0d-72f2-4dec-83a4-a9c702888d65",
   "metadata": {},
   "source": [
    "### Cumulative log odds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "c49d693c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(1, 2, figsize=(10, 5))\n",
    "\n",
    "response_probs = response_counts / response_counts.sum()\n",
    "cumulative_probs = response_probs.cumsum()\n",
    "\n",
    "# Plot  Cumulative proportion\n",
    "plt.sca(axs[0])\n",
    "plt.bar(cumulative_probs.index, cumulative_probs.values, color=\"k\", width=0.25)\n",
    "offsets = cumulative_probs.values - response_probs.values\n",
    "plt.bar(cumulative_probs.index, response_probs.values, bottom=offsets, color=\"C0\", width=0.25)\n",
    "\n",
    "offsets = np.append(offsets, 1)  # add p = 1 condition\n",
    "for ii, y in enumerate(offsets[1:]):\n",
    "    x1 = ii + 1\n",
    "    plt.plot((0, x1), (y, y), color=\"C0\", linestyle=\"--\")\n",
    "\n",
    "plt.xlabel(\"outcome\")\n",
    "plt.xticks(xticks)\n",
    "plt.xlim([0, 7.5])\n",
    "plt.ylabel(\"cumulative proportion\")\n",
    "plt.ylim([0, 1.05])\n",
    "\n",
    "# Cumulative proportion vs cumulative log-odds\n",
    "infty = 3.5  # \"Infinity\" log odds x-value for plots\n",
    "neg_infty = -1 * infty\n",
    "cumulative_log_odds = [np.log(p / (1 - p)) for p in cumulative_probs]\n",
    "cumulative_log_odds[-1] = infty\n",
    "\n",
    "# Plot cumulative data points\n",
    "plt.sca(axs[1])\n",
    "plt.scatter(cumulative_log_odds, cumulative_probs.values, color=\"C1\", s=100)\n",
    "plt.scatter(infty, 1, color=\"gray\", s=100)  # add point for Infinity\n",
    "for ii, (clo, clp) in enumerate(zip(cumulative_log_odds, cumulative_probs.values)):\n",
    "\n",
    "    # Add cumulative proportion\n",
    "    plt.plot((neg_infty, clo), (clp, clp), color=\"C0\", linestyle=\"--\")\n",
    "\n",
    "    # Add & label cutpoints\n",
    "    if ii < N_RESPONSE_CATEGORIES - 1:\n",
    "        plt.plot((clo, clo), (0, clp), color=\"C1\", linestyle=\"--\")\n",
    "        plt.text(clo + 0.05, 0.025, f\"$\\\\alpha_{ii+1}$\", color=\"C1\", fontsize=10)\n",
    "    else:\n",
    "        plt.plot((clo, clo), (0, clp), color=\"grey\", linestyle=\"--\")\n",
    "\n",
    "    # Annotate data points\n",
    "    plt.text(clo + 0.05, clp + 0.02, f\"{ii+1}\", color=\"C1\", fontsize=20)\n",
    "\n",
    "plt.xlabel(\"cumulative log-odds\")\n",
    "plt.xlim([neg_infty, infty + 0.5])\n",
    "plt.xticks([-2, 0, 2, infty], labels=[-2, 0, 2, \"$\\\\infty$\"])\n",
    "plt.ylabel(\"cumulative proportion\")\n",
    "plt.ylim([0, 1.05])\n",
    "\n",
    "plt.sca(axs[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b98b67f4",
   "metadata": {},
   "source": [
    "### Ordering responses with CDF\n",
    "\n",
    "Using the CDF, we can establish cut points $\\alpha_R$ on the cumulative log odds that correspond with the cumulative probability of that response (or one smaller). Thus the CDF gives us a proxy for order.\n",
    "\n",
    "### Calculating $P(R_i=k)$\n",
    "\n",
    "$$\n",
    "P(R_i=k) = P(R_i \\le k ) - P(R_i \\le k - 1)\n",
    "$$\n",
    "\n",
    "For example, for $k = 3$\n",
    "$$\n",
    "P(R_i=3) = P(R_i\\le3) - P(R_i\\le2)\n",
    "$$\n",
    "\n",
    "### Setting up the GLM\n",
    "\n",
    "We model the _cumulative_ log odds as each break point $\\alpha_k$\n",
    "\n",
    "$$\n",
    "\\log \\frac{P(R<=k)}{1 - P(R<=k)} = \\alpha_k\n",
    "$$\n",
    "\n",
    "### Where's the GLM?\n",
    "\n",
    "How can we make this a function of predictors\n",
    "\n",
    "1. Have a $\\alpha_k$ for each predictor variable\n",
    "2. Use an offset $\\phi_i$ for each data point that is a function of the predictors\n",
    "\n",
    "$$\n",
    "\\begin{align*}\n",
    "\\log \\frac{P(R<=k)}{1 - P(R<=k)} &= \\alpha_k + \\phi_i \\\\\n",
    "\\phi_i &= \\beta_A A_i + \\beta_C C_i + \\beta_I I_i \\\\\n",
    "\\end{align*}\n",
    "$$\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "21b69596",
   "metadata": {},
   "source": [
    "### Demonstrating the Effect of $\\phi$ on Response Distribution\n",
    "Changing $\\phi$ \"squishes\" or \"stretches\" the cumulative histogram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "ea81fe69",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x1400 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_cumulative_log_odds_and_cumulative_probs(cumulative_log_odds, cumulative_probs, phi):\n",
    "    infity = max(cumulative_log_odds) + 1\n",
    "    neg_infity = min(cumulative_log_odds) - 1\n",
    "\n",
    "    # Plot cumulative data points\n",
    "    plt.scatter(cumulative_log_odds[:-2], cumulative_probs[:-2], color=\"C1\", s=100)\n",
    "    plt.scatter(infity, 1, color=\"gray\", s=100)  # add point for Infinity\n",
    "\n",
    "    for ii, (clo, clp) in enumerate(zip(cumulative_log_odds, cumulative_probs)):\n",
    "\n",
    "        # Add & label cutpoints\n",
    "        if ii < N_RESPONSE_CATEGORIES - 1:\n",
    "            plt.plot((clo, clo), (0, clp), color=\"C1\", linestyle=\"--\")\n",
    "            # Add cumulative proportion\n",
    "            plt.plot((neg_infity, clo), (clp, clp), color=\"C0\", linestyle=\"--\")\n",
    "            # Annotate data points\n",
    "            plt.text(clo + 0.05, clp + 0.02, f\"{ii+1}\", color=\"C1\", fontsize=14)\n",
    "        else:\n",
    "            plt.plot((infity, infity), (0, 1), color=\"grey\", linestyle=\"--\")\n",
    "            plt.plot((neg_infity, infity), (1, 1), color=\"C0\", linestyle=\"--\")\n",
    "            # Annotate data points\n",
    "            plt.text(infity + 0.05, 1 + 0.02, f\"{ii+1}\", color=\"C1\", fontsize=14)\n",
    "\n",
    "    plt.xlabel(\"cumulative log-odds\")\n",
    "    plt.xlim([neg_infity, infity + 0.5])\n",
    "    if phi < 0:\n",
    "        xticks = [neg_infity, phi, 0, infity]\n",
    "        xtick_labels = [\"-$\\\\infty$\", \"$\\\\phi$\", 0, \"$\\\\infty$\"]\n",
    "    elif phi > 0:\n",
    "        xticks = [neg_infity, 0, phi, infity]\n",
    "        xtick_labels = [\"-$\\\\infty$\", 0, \"$\\\\phi$\", \"$\\\\infty$\"]\n",
    "    else:\n",
    "        xticks = [neg_infity, phi, infity]\n",
    "        xtick_labels = [\"-$\\\\infty$\", \"$\\\\phi$\", \"$\\\\infty$\"]\n",
    "\n",
    "    plt.xticks(xticks, labels=xtick_labels)\n",
    "    plt.ylabel(\"cumulative proportion\")\n",
    "    plt.ylim([-0.05, 1.05])\n",
    "\n",
    "\n",
    "def plot_cumulative_probs_bar(cumulative_probs):\n",
    "    xs = range(1, len(cumulative_probs) + 1)\n",
    "    diff_probs = [c for c in cumulative_probs]\n",
    "    diff_probs.insert(0, 0)\n",
    "    probs = np.diff(diff_probs)\n",
    "    plt.bar(xs, probs, color=\"C0\", width=0.25)\n",
    "\n",
    "\n",
    "def show_phi_effect(cumulative_log_odds, phi=0):\n",
    "    if not isinstance(phi, list):\n",
    "        phi = [phi]\n",
    "    n_phis = len(phi)\n",
    "\n",
    "    fig, axs = plt.subplots(n_phis, 2, figsize=(10, 8 + n_phis * 2))\n",
    "\n",
    "    for ii, ph in enumerate(phi):\n",
    "\n",
    "        # Shift the cumulative log odds by phi\n",
    "        cumulative_log_odds_plus_phi = [v + ph for v in cumulative_log_odds]\n",
    "\n",
    "        # Resulting cumulative probabilities\n",
    "        cumulative_probs_plus_phi = [utils.invlogit(c) for c in cumulative_log_odds_plus_phi]\n",
    "\n",
    "        plt.sca(axs[ii][0])\n",
    "\n",
    "        plot_cumulative_log_odds_and_cumulative_probs(\n",
    "            cumulative_log_odds=cumulative_log_odds_plus_phi,\n",
    "            cumulative_probs=cumulative_probs_plus_phi,\n",
    "            phi=ph,\n",
    "        )\n",
    "        plt.scatter(ph, 0, s=100, color=\"k\")\n",
    "        plt.title(f\"Transformed Cumulatives $\\\\phi={ph}$\")\n",
    "\n",
    "        plt.sca(axs[ii][1])\n",
    "        plot_cumulative_probs_bar(cumulative_probs_plus_phi)\n",
    "        plt.title(f\"Transformed Histogram $\\\\phi={ph}$\")\n",
    "\n",
    "\n",
    "show_phi_effect(cumulative_log_odds, phi=[0, 2, -2])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c4360d55",
   "metadata": {},
   "source": [
    "## Statistical Model\n",
    "### Starting off easy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "79ef2929-2dc2-44f7-9f8b-4d38170239d8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/svg+xml": [
       "<?xml version=\"1.0\" encoding=\"UTF-8\" standalone=\"no\"?>\n",
       "<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n",
       " \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\n",
       "<!-- Generated by graphviz version 12.0.0 (20240803.0821)\n",
       " -->\n",
       "<!-- Pages: 1 -->\n",
       "<svg width=\"610pt\" height=\"231pt\"\n",
       " viewBox=\"0.00 0.00 610.33 230.72\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n",
       "<g id=\"graph0\" class=\"graph\" transform=\"scale(1 1) rotate(0) translate(4 226.72)\">\n",
       "<polygon fill=\"white\" stroke=\"none\" points=\"-4,4 -4,-226.72 606.33,-226.72 606.33,4 -4,4\"/>\n",
       "<!-- X -->\n",
       "<g id=\"node1\" class=\"node\">\n",
       "<title>X</title>\n",
       "<ellipse fill=\"none\" stroke=\"red\" cx=\"291.09\" cy=\"-203.28\" rx=\"145.49\" ry=\"19.45\"/>\n",
       "<text text-anchor=\"middle\" x=\"291.09\" y=\"-199.73\" font-family=\"Times,serif\" font-size=\"14.00\">action, intenition, contact, X</text>\n",
       "</g>\n",
       "<!-- R -->\n",
       "<g id=\"node2\" class=\"node\">\n",
       "<title>R</title>\n",
       "<ellipse fill=\"none\" stroke=\"red\" cx=\"537.45\" cy=\"-99.28\" rx=\"64.88\" ry=\"19.45\"/>\n",
       "<text text-anchor=\"middle\" x=\"537.45\" y=\"-95.73\" font-family=\"Times,serif\" font-size=\"14.00\">response, R</text>\n",
       "</g>\n",
       "<!-- X&#45;&gt;R -->\n",
       "<g id=\"edge1\" class=\"edge\">\n",
       "<title>X&#45;&gt;R</title>\n",
       "<path fill=\"none\" stroke=\"red\" d=\"M393,-189.05C407.95,-185.23 422.92,-180.4 436.58,-174.28 463.4,-162.27 489.82,-141.95 508.74,-125.49\"/>\n",
       "<polygon fill=\"red\" stroke=\"red\" points=\"510.87,-128.28 516.01,-119.02 506.21,-123.06 510.87,-128.28\"/>\n",
       "</g>\n",
       "<!-- S -->\n",
       "<g id=\"node3\" class=\"node\">\n",
       "<title>S</title>\n",
       "<ellipse fill=\"none\" stroke=\"black\" cx=\"291.09\" cy=\"-146.28\" rx=\"45.25\" ry=\"19.45\"/>\n",
       "<text text-anchor=\"middle\" x=\"291.09\" y=\"-142.73\" font-family=\"Times,serif\" font-size=\"14.00\">story, S</text>\n",
       "</g>\n",
       "<!-- S&#45;&gt;R -->\n",
       "<g id=\"edge2\" class=\"edge\">\n",
       "<title>S&#45;&gt;R</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M332.99,-138.41C370.61,-131.18 427.05,-120.32 470.95,-111.88\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"471.46,-115.34 480.62,-110.02 470.14,-108.47 471.46,-115.34\"/>\n",
       "</g>\n",
       "<!-- E -->\n",
       "<g id=\"node4\" class=\"node\">\n",
       "<title>E</title>\n",
       "<ellipse fill=\"none\" stroke=\"black\" cx=\"291.09\" cy=\"-52.28\" rx=\"70.18\" ry=\"19.45\"/>\n",
       "<text text-anchor=\"middle\" x=\"291.09\" y=\"-48.73\" font-family=\"Times,serif\" font-size=\"14.00\">education, E</text>\n",
       "</g>\n",
       "<!-- E&#45;&gt;R -->\n",
       "<g id=\"edge3\" class=\"edge\">\n",
       "<title>E&#45;&gt;R</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M349.69,-63.36C386.16,-70.37 433.41,-79.46 471.26,-86.74\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"470.2,-90.1 480.68,-88.55 471.53,-83.23 470.2,-90.1\"/>\n",
       "</g>\n",
       "<!-- Y -->\n",
       "<g id=\"node5\" class=\"node\">\n",
       "<title>Y</title>\n",
       "<ellipse fill=\"none\" stroke=\"black\" cx=\"54.8\" cy=\"-89.28\" rx=\"39.42\" ry=\"19.45\"/>\n",
       "<text text-anchor=\"middle\" x=\"54.8\" y=\"-85.73\" font-family=\"Times,serif\" font-size=\"14.00\">age, Y</text>\n",
       "</g>\n",
       "<!-- Y&#45;&gt;R -->\n",
       "<g id=\"edge4\" class=\"edge\">\n",
       "<title>Y&#45;&gt;R</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M94.32,-90.08C173.77,-91.73 357.05,-95.55 460.97,-97.71\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"460.73,-101.2 470.8,-97.91 460.87,-94.21 460.73,-101.2\"/>\n",
       "</g>\n",
       "<!-- Y&#45;&gt;E -->\n",
       "<g id=\"edge6\" class=\"edge\">\n",
       "<title>Y&#45;&gt;E</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M92.92,-83.42C126.49,-78.12 176.85,-70.16 218.27,-63.62\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"218.59,-67.11 227.92,-62.1 217.5,-60.2 218.59,-67.11\"/>\n",
       "</g>\n",
       "<!-- G -->\n",
       "<g id=\"node6\" class=\"node\">\n",
       "<title>G</title>\n",
       "<ellipse fill=\"none\" stroke=\"black\" cx=\"54.8\" cy=\"-23.28\" rx=\"54.8\" ry=\"19.45\"/>\n",
       "<text text-anchor=\"middle\" x=\"54.8\" y=\"-19.73\" font-family=\"Times,serif\" font-size=\"14.00\">gender, G</text>\n",
       "</g>\n",
       "<!-- G&#45;&gt;R -->\n",
       "<g id=\"edge5\" class=\"edge\">\n",
       "<title>G&#45;&gt;R</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M103.65,-14C177.37,-1.76 322.18,14.06 436.58,-24.28 464.98,-33.8 491.8,-54.88 510.49,-72.27\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"508.01,-74.74 517.64,-79.15 512.86,-69.69 508.01,-74.74\"/>\n",
       "</g>\n",
       "<!-- G&#45;&gt;E -->\n",
       "<g id=\"edge7\" class=\"edge\">\n",
       "<title>G&#45;&gt;E</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M106.93,-29.61C138.64,-33.53 179.93,-38.64 215.28,-43.02\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"214.67,-46.47 225.03,-44.23 215.53,-39.52 214.67,-46.47\"/>\n",
       "</g>\n",
       "</g>\n",
       "</svg>\n"
      ],
      "text/plain": [
       "<graphviz.graphs.Digraph at 0x7f33fe49cfe0>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "utils.draw_causal_graph(\n",
    "    edge_list=[(\"X\", \"R\"), (\"S\", \"R\"), (\"E\", \"R\"), (\"Y\", \"R\"), (\"G\", \"R\"), (\"Y\", \"E\"), (\"G\", \"E\")],\n",
    "    node_props={\n",
    "        \"X\": {\"color\": \"red\", \"label\": \"action, intenition, contact, X\"},\n",
    "        \"R\": {\"color\": \"red\", \"label\": \"response, R\"},\n",
    "        \"S\": {\"label\": \"story, S\"},\n",
    "        \"E\": {\"label\": \"education, E\"},\n",
    "        \"Y\": {\"label\": \"age, Y\"},\n",
    "        \"G\": {\"label\": \"gender, G\"},\n",
    "    },\n",
    "    edge_props={\n",
    "        (\"X\", \"R\"): {\"color\": \"red\"},\n",
    "    },\n",
    "    graph_direction=\"LR\",\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "40cf857a-a9ae-4ea3-9d28-c1e38bbd6369",
   "metadata": {},
   "source": [
    "$$\n",
    "\\begin{align*}\n",
    "R_i &\\sim \\text{OrderedLogit}(\\phi_i, \\alpha) \\\\\n",
    "\\phi_i &= \\beta_A A_i + \\beta_C C_i + \\beta_I I_i \\\\\n",
    "\\alpha_j &\\sim \\mathcal N(0,1) \\\\\n",
    "\\beta_{A,C,I} &\\sim \\mathcal N(0, .5)\n",
    "\\end{align*}\n",
    "$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "b2e7b06d-af0c-475e-8d3a-881e49005f73",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/svg+xml": [
       "<?xml version=\"1.0\" encoding=\"UTF-8\" standalone=\"no\"?>\n",
       "<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n",
       " \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\n",
       "<!-- Generated by graphviz version 12.0.0 (20240803.0821)\n",
       " -->\n",
       "<!-- Pages: 1 -->\n",
       "<svg width=\"979pt\" height=\"280pt\"\n",
       " viewBox=\"0.00 0.00 979.00 279.55\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n",
       "<g id=\"graph0\" class=\"graph\" transform=\"scale(1 1) rotate(0) translate(4 275.55)\">\n",
       "<polygon fill=\"white\" stroke=\"none\" points=\"-4,4 -4,-275.55 975,-275.55 975,4 -4,4\"/>\n",
       "<g id=\"clust1\" class=\"cluster\">\n",
       "<title>cluster9930</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M20,-126C20,-126 376,-126 376,-126 382,-126 388,-132 388,-138 388,-138 388,-251.55 388,-251.55 388,-257.55 382,-263.55 376,-263.55 376,-263.55 20,-263.55 20,-263.55 14,-263.55 8,-257.55 8,-251.55 8,-251.55 8,-138 8,-138 8,-132 14,-126 20,-126\"/>\n",
       "<text text-anchor=\"middle\" x=\"365\" y=\"-136.2\" font-family=\"Times,serif\" font-size=\"14.00\">9930</text>\n",
       "</g>\n",
       "<g id=\"clust2\" class=\"cluster\">\n",
       "<title>clusterCUTPOINTS (6)</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M863,-126C863,-126 951,-126 951,-126 957,-126 963,-132 963,-138 963,-138 963,-251.55 963,-251.55 963,-257.55 957,-263.55 951,-263.55 951,-263.55 863,-263.55 863,-263.55 857,-263.55 851,-257.55 851,-251.55 851,-251.55 851,-138 851,-138 851,-132 857,-126 863,-126\"/>\n",
       "<text text-anchor=\"middle\" x=\"906.62\" y=\"-136.2\" font-family=\"Times,serif\" font-size=\"14.00\">CUTPOINTS (6)</text>\n",
       "</g>\n",
       "<g id=\"clust3\" class=\"cluster\">\n",
       "<title>cluster9930 x 7</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M355,-8C355,-8 467,-8 467,-8 473,-8 479,-14 479,-20 479,-20 479,-106 479,-106 479,-112 473,-118 467,-118 467,-118 355,-118 355,-118 349,-118 343,-112 343,-106 343,-106 343,-20 343,-20 343,-14 349,-8 355,-8\"/>\n",
       "<text text-anchor=\"middle\" x=\"444.38\" y=\"-18.2\" font-family=\"Times,serif\" font-size=\"14.00\">9930 x 7</text>\n",
       "</g>\n",
       "<!-- action -->\n",
       "<g id=\"node1\" class=\"node\">\n",
       "<title>action</title>\n",
       "<path fill=\"lightgrey\" stroke=\"black\" d=\"M62,-241.77C62,-241.77 28,-241.77 28,-241.77 22,-241.77 16,-235.77 16,-229.77 16,-229.77 16,-187.27 16,-187.27 16,-181.27 22,-175.27 28,-175.27 28,-175.27 62,-175.27 62,-175.27 68,-175.27 74,-181.27 74,-187.27 74,-187.27 74,-229.77 74,-229.77 74,-235.77 68,-241.77 62,-241.77\"/>\n",
       "<text text-anchor=\"middle\" x=\"45\" y=\"-224.47\" font-family=\"Times,serif\" font-size=\"14.00\">action</text>\n",
       "<text text-anchor=\"middle\" x=\"45\" y=\"-204.97\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
       "<text text-anchor=\"middle\" x=\"45\" y=\"-185.47\" font-family=\"Times,serif\" font-size=\"14.00\">Data</text>\n",
       "</g>\n",
       "<!-- response_probs -->\n",
       "<g id=\"node9\" class=\"node\">\n",
       "<title>response_probs</title>\n",
       "<polygon fill=\"none\" stroke=\"black\" points=\"470.75,-110 351.25,-110 351.25,-43.5 470.75,-43.5 470.75,-110\"/>\n",
       "<text text-anchor=\"middle\" x=\"411\" y=\"-92.7\" font-family=\"Times,serif\" font-size=\"14.00\">response_probs</text>\n",
       "<text text-anchor=\"middle\" x=\"411\" y=\"-73.2\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
       "<text text-anchor=\"middle\" x=\"411\" y=\"-53.7\" font-family=\"Times,serif\" font-size=\"14.00\">Deterministic</text>\n",
       "</g>\n",
       "<!-- action&#45;&gt;response_probs -->\n",
       "<g id=\"edge2\" class=\"edge\">\n",
       "<title>action&#45;&gt;response_probs</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M52.24,-174.9C57.77,-157.69 67.22,-137.76 83,-126 122.49,-96.57 255.76,-84.93 339.78,-80.44\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"339.72,-83.95 349.53,-79.94 339.36,-76.96 339.72,-83.95\"/>\n",
       "</g>\n",
       "<!-- contact -->\n",
       "<g id=\"node2\" class=\"node\">\n",
       "<title>contact</title>\n",
       "<path fill=\"lightgrey\" stroke=\"black\" d=\"M145.75,-241.77C145.75,-241.77 104.25,-241.77 104.25,-241.77 98.25,-241.77 92.25,-235.77 92.25,-229.77 92.25,-229.77 92.25,-187.27 92.25,-187.27 92.25,-181.27 98.25,-175.27 104.25,-175.27 104.25,-175.27 145.75,-175.27 145.75,-175.27 151.75,-175.27 157.75,-181.27 157.75,-187.27 157.75,-187.27 157.75,-229.77 157.75,-229.77 157.75,-235.77 151.75,-241.77 145.75,-241.77\"/>\n",
       "<text text-anchor=\"middle\" x=\"125\" y=\"-224.47\" font-family=\"Times,serif\" font-size=\"14.00\">contact</text>\n",
       "<text text-anchor=\"middle\" x=\"125\" y=\"-204.97\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
       "<text text-anchor=\"middle\" x=\"125\" y=\"-185.47\" font-family=\"Times,serif\" font-size=\"14.00\">Data</text>\n",
       "</g>\n",
       "<!-- contact&#45;&gt;response_probs -->\n",
       "<g id=\"edge3\" class=\"edge\">\n",
       "<title>contact&#45;&gt;response_probs</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M134.09,-175.14C140.55,-158 150.97,-138.06 167,-126 193.76,-105.86 277.65,-92.42 339.63,-84.94\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"339.9,-88.44 349.43,-83.79 339.09,-81.48 339.9,-88.44\"/>\n",
       "</g>\n",
       "<!-- response -->\n",
       "<g id=\"node3\" class=\"node\">\n",
       "<title>response</title>\n",
       "<ellipse fill=\"lightgrey\" stroke=\"black\" cx=\"241\" cy=\"-208.52\" rx=\"64.88\" ry=\"47.02\"/>\n",
       "<text text-anchor=\"middle\" x=\"241\" y=\"-224.47\" font-family=\"Times,serif\" font-size=\"14.00\">response</text>\n",
       "<text text-anchor=\"middle\" x=\"241\" y=\"-204.97\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
       "<text text-anchor=\"middle\" x=\"241\" y=\"-185.47\" font-family=\"Times,serif\" font-size=\"14.00\">Categorical</text>\n",
       "</g>\n",
       "<!-- intent -->\n",
       "<g id=\"node4\" class=\"node\">\n",
       "<title>intent</title>\n",
       "<path fill=\"lightgrey\" stroke=\"black\" d=\"M368.25,-241.77C368.25,-241.77 335.75,-241.77 335.75,-241.77 329.75,-241.77 323.75,-235.77 323.75,-229.77 323.75,-229.77 323.75,-187.27 323.75,-187.27 323.75,-181.27 329.75,-175.27 335.75,-175.27 335.75,-175.27 368.25,-175.27 368.25,-175.27 374.25,-175.27 380.25,-181.27 380.25,-187.27 380.25,-187.27 380.25,-229.77 380.25,-229.77 380.25,-235.77 374.25,-241.77 368.25,-241.77\"/>\n",
       "<text text-anchor=\"middle\" x=\"352\" y=\"-224.47\" font-family=\"Times,serif\" font-size=\"14.00\">intent</text>\n",
       "<text text-anchor=\"middle\" x=\"352\" y=\"-204.97\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
       "<text text-anchor=\"middle\" x=\"352\" y=\"-185.47\" font-family=\"Times,serif\" font-size=\"14.00\">Data</text>\n",
       "</g>\n",
       "<!-- intent&#45;&gt;response_probs -->\n",
       "<g id=\"edge4\" class=\"edge\">\n",
       "<title>intent&#45;&gt;response_probs</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M366.75,-174.99C373.52,-160.06 381.66,-142.13 389,-126 389.79,-124.27 390.59,-122.51 391.4,-120.73\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"394.49,-122.38 395.45,-111.83 388.12,-119.48 394.49,-122.38\"/>\n",
       "</g>\n",
       "<!-- beta_contact -->\n",
       "<g id=\"node5\" class=\"node\">\n",
       "<title>beta_contact</title>\n",
       "<ellipse fill=\"none\" stroke=\"black\" cx=\"470\" cy=\"-208.52\" rx=\"71.77\" ry=\"47.02\"/>\n",
       "<text text-anchor=\"middle\" x=\"470\" y=\"-224.47\" font-family=\"Times,serif\" font-size=\"14.00\">beta_contact</text>\n",
       "<text text-anchor=\"middle\" x=\"470\" y=\"-204.97\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
       "<text text-anchor=\"middle\" x=\"470\" y=\"-185.47\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
       "</g>\n",
       "<!-- beta_contact&#45;&gt;response_probs -->\n",
       "<g id=\"edge1\" class=\"edge\">\n",
       "<title>beta_contact&#45;&gt;response_probs</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M449.86,-163.23C443.58,-149.42 436.67,-134.22 430.44,-120.52\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"433.79,-119.41 426.46,-111.76 427.41,-122.31 433.79,-119.41\"/>\n",
       "</g>\n",
       "<!-- beta_action -->\n",
       "<g id=\"node6\" class=\"node\">\n",
       "<title>beta_action</title>\n",
       "<ellipse fill=\"none\" stroke=\"black\" cx=\"626\" cy=\"-208.52\" rx=\"66.47\" ry=\"47.02\"/>\n",
       "<text text-anchor=\"middle\" x=\"626\" y=\"-224.47\" font-family=\"Times,serif\" font-size=\"14.00\">beta_action</text>\n",
       "<text text-anchor=\"middle\" x=\"626\" y=\"-204.97\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
       "<text text-anchor=\"middle\" x=\"626\" y=\"-185.47\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
       "</g>\n",
       "<!-- beta_action&#45;&gt;response_probs -->\n",
       "<g id=\"edge5\" class=\"edge\">\n",
       "<title>beta_action&#45;&gt;response_probs</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M595.41,-166.59C582.95,-152.16 567.57,-136.85 551,-126 530.2,-112.39 505.14,-102.18 482.01,-94.74\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"483.22,-91.45 472.63,-91.85 481.16,-98.14 483.22,-91.45\"/>\n",
       "</g>\n",
       "<!-- beta_intent -->\n",
       "<g id=\"node7\" class=\"node\">\n",
       "<title>beta_intent</title>\n",
       "<ellipse fill=\"none\" stroke=\"black\" cx=\"776\" cy=\"-208.52\" rx=\"65.41\" ry=\"47.02\"/>\n",
       "<text text-anchor=\"middle\" x=\"776\" y=\"-224.47\" font-family=\"Times,serif\" font-size=\"14.00\">beta_intent</text>\n",
       "<text text-anchor=\"middle\" x=\"776\" y=\"-204.97\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
       "<text text-anchor=\"middle\" x=\"776\" y=\"-185.47\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
       "</g>\n",
       "<!-- beta_intent&#45;&gt;response_probs -->\n",
       "<g id=\"edge7\" class=\"edge\">\n",
       "<title>beta_intent&#45;&gt;response_probs</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M747.04,-165.92C734.57,-151.05 718.76,-135.61 701,-126 664.17,-106.07 555.42,-91.87 482.08,-84.24\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"482.88,-80.8 472.58,-83.27 482.17,-87.77 482.88,-80.8\"/>\n",
       "</g>\n",
       "<!-- alpha -->\n",
       "<g id=\"node8\" class=\"node\">\n",
       "<title>alpha</title>\n",
       "<ellipse fill=\"none\" stroke=\"black\" cx=\"907\" cy=\"-208.52\" rx=\"47.91\" ry=\"47.02\"/>\n",
       "<text text-anchor=\"middle\" x=\"907\" y=\"-224.47\" font-family=\"Times,serif\" font-size=\"14.00\">alpha</text>\n",
       "<text text-anchor=\"middle\" x=\"907\" y=\"-204.97\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
       "<text text-anchor=\"middle\" x=\"907\" y=\"-185.47\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
       "</g>\n",
       "<!-- alpha&#45;&gt;response_probs -->\n",
       "<g id=\"edge6\" class=\"edge\">\n",
       "<title>alpha&#45;&gt;response_probs</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M887.18,-165.54C877.99,-150.62 865.6,-135.25 850,-126 789.54,-90.17 590.56,-80.91 482.44,-78.54\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"482.58,-75.05 472.51,-78.34 482.44,-82.04 482.58,-75.05\"/>\n",
       "</g>\n",
       "<!-- response_probs&#45;&gt;response -->\n",
       "<g id=\"edge8\" class=\"edge\">\n",
       "<title>response_probs&#45;&gt;response</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M350.84,-103.71C338.48,-110.2 325.92,-117.71 315,-126 302.51,-135.49 290.42,-147.28 279.76,-158.91\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"277.31,-156.39 273.27,-166.19 282.54,-161.05 277.31,-156.39\"/>\n",
       "</g>\n",
       "</g>\n",
       "</svg>\n"
      ],
      "text/plain": [
       "<graphviz.graphs.Digraph at 0x7f33f12bac60>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Set up data / coords\n",
    "RESPONSE_ID, RESPONSE = pd.factorize(TROLLEY.response.astype(int), sort=True)\n",
    "N_RESPONSE_CLASSES = len(RESPONSE)\n",
    "CUTPOINTS = np.arange(1, N_RESPONSE_CLASSES).astype(int)\n",
    "\n",
    "# for labeling cutpoints\n",
    "coords = {\"CUTPOINTS\": CUTPOINTS}\n",
    "with pm.Model(coords=coords) as basic_ordered_logistic_model:\n",
    "\n",
    "    # Data for posetior predictions\n",
    "    action = pm.Data(\"action\", TROLLEY.action.astype(float))\n",
    "    intent = pm.Data(\"intent\", TROLLEY.intention.astype(float))\n",
    "    contact = pm.Data(\"contact\", TROLLEY.contact.astype(float))\n",
    "\n",
    "    # Priors\n",
    "    beta_action = pm.Normal(\"beta_action\", 0, 0.5)\n",
    "    beta_intent = pm.Normal(\"beta_intent\", 0, 0.5)\n",
    "    beta_contact = pm.Normal(\"beta_contact\", 0, 0.5)\n",
    "\n",
    "    cutpoints = pm.Normal(\n",
    "        \"alpha\",\n",
    "        mu=0,\n",
    "        sigma=1,\n",
    "        transform=pm.distributions.transforms.univariate_ordered,\n",
    "        shape=N_RESPONSE_CLASSES - 1,\n",
    "        initval=np.arange(N_RESPONSE_CLASSES - 1)\n",
    "        - 2.5,  # use ordering (with coarse log-odds centering) for init\n",
    "        dims=\"CUTPOINTS\",\n",
    "    )\n",
    "\n",
    "    # Likelihood\n",
    "    phi = beta_action * action + beta_intent * intent + beta_contact * contact\n",
    "    pm.OrderedLogistic(\"response\", cutpoints=cutpoints, eta=phi, observed=RESPONSE_ID)\n",
    "\n",
    "pm.model_to_graphviz(basic_ordered_logistic_model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "9f2993ba",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Initializing NUTS using jitter+adapt_diag...\n",
      "Multiprocess sampling (4 chains in 4 jobs)\n",
      "NUTS: [beta_action, beta_intent, beta_contact, alpha]\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "949b78fb687b4debaf8003743e7921a8",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Sampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 38 seconds.\n"
     ]
    }
   ],
   "source": [
    "with basic_ordered_logistic_model:\n",
    "    basic_ordered_logistic_inference = pm.sample()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "934e630c-e5f4-429b-8b91-0b1891140a36",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>sd</th>\n",
       "      <th>hdi_3%</th>\n",
       "      <th>hdi_97%</th>\n",
       "      <th>mcse_mean</th>\n",
       "      <th>mcse_sd</th>\n",
       "      <th>ess_bulk</th>\n",
       "      <th>ess_tail</th>\n",
       "      <th>r_hat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>beta_contact</th>\n",
       "      <td>-0.943</td>\n",
       "      <td>0.050</td>\n",
       "      <td>-1.032</td>\n",
       "      <td>-0.849</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.001</td>\n",
       "      <td>3029.0</td>\n",
       "      <td>2894.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>beta_intent</th>\n",
       "      <td>-0.711</td>\n",
       "      <td>0.036</td>\n",
       "      <td>-0.777</td>\n",
       "      <td>-0.644</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.000</td>\n",
       "      <td>3348.0</td>\n",
       "      <td>3184.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>beta_action</th>\n",
       "      <td>-0.695</td>\n",
       "      <td>0.040</td>\n",
       "      <td>-0.770</td>\n",
       "      <td>-0.622</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.001</td>\n",
       "      <td>2522.0</td>\n",
       "      <td>3032.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>alpha[1]</th>\n",
       "      <td>-2.819</td>\n",
       "      <td>0.046</td>\n",
       "      <td>-2.898</td>\n",
       "      <td>-2.726</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.001</td>\n",
       "      <td>1769.0</td>\n",
       "      <td>2416.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>alpha[2]</th>\n",
       "      <td>-2.138</td>\n",
       "      <td>0.041</td>\n",
       "      <td>-2.214</td>\n",
       "      <td>-2.058</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.001</td>\n",
       "      <td>1919.0</td>\n",
       "      <td>2408.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>alpha[3]</th>\n",
       "      <td>-1.556</td>\n",
       "      <td>0.039</td>\n",
       "      <td>-1.625</td>\n",
       "      <td>-1.479</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.001</td>\n",
       "      <td>2038.0</td>\n",
       "      <td>2547.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>alpha[4]</th>\n",
       "      <td>-0.537</td>\n",
       "      <td>0.036</td>\n",
       "      <td>-0.610</td>\n",
       "      <td>-0.472</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.001</td>\n",
       "      <td>2402.0</td>\n",
       "      <td>3133.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>alpha[5]</th>\n",
       "      <td>0.131</td>\n",
       "      <td>0.037</td>\n",
       "      <td>0.066</td>\n",
       "      <td>0.204</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.001</td>\n",
       "      <td>2475.0</td>\n",
       "      <td>2695.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>alpha[6]</th>\n",
       "      <td>1.037</td>\n",
       "      <td>0.040</td>\n",
       "      <td>0.960</td>\n",
       "      <td>1.108</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.001</td>\n",
       "      <td>2825.0</td>\n",
       "      <td>3296.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               mean     sd  hdi_3%  hdi_97%  mcse_mean  mcse_sd  ess_bulk  \\\n",
       "beta_contact -0.943  0.050  -1.032   -0.849      0.001    0.001    3029.0   \n",
       "beta_intent  -0.711  0.036  -0.777   -0.644      0.001    0.000    3348.0   \n",
       "beta_action  -0.695  0.040  -0.770   -0.622      0.001    0.001    2522.0   \n",
       "alpha[1]     -2.819  0.046  -2.898   -2.726      0.001    0.001    1769.0   \n",
       "alpha[2]     -2.138  0.041  -2.214   -2.058      0.001    0.001    1919.0   \n",
       "alpha[3]     -1.556  0.039  -1.625   -1.479      0.001    0.001    2038.0   \n",
       "alpha[4]     -0.537  0.036  -0.610   -0.472      0.001    0.001    2402.0   \n",
       "alpha[5]      0.131  0.037   0.066    0.204      0.001    0.001    2475.0   \n",
       "alpha[6]      1.037  0.040   0.960    1.108      0.001    0.001    2825.0   \n",
       "\n",
       "              ess_tail  r_hat  \n",
       "beta_contact    2894.0    1.0  \n",
       "beta_intent     3184.0    1.0  \n",
       "beta_action     3032.0    1.0  \n",
       "alpha[1]        2416.0    1.0  \n",
       "alpha[2]        2408.0    1.0  \n",
       "alpha[3]        2547.0    1.0  \n",
       "alpha[4]        3133.0    1.0  \n",
       "alpha[5]        2695.0    1.0  \n",
       "alpha[6]        3296.0    1.0  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "az.summary(\n",
    "    basic_ordered_logistic_inference,\n",
    "    var_names=[\"beta_contact\", \"beta_intent\", \"beta_action\", \"alpha\"],\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3ac257ed",
   "metadata": {},
   "source": [
    "### Posterior Predictive Distributions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "66e20423",
   "metadata": {},
   "outputs": [],
   "source": [
    "def run_basic_trolley_counterfactual(action=0, intent=0, contact=0, ax=None):\n",
    "    with basic_ordered_logistic_model:\n",
    "        pm.set_data(\n",
    "            {\n",
    "                \"action\": xr.DataArray([action] * N_TROLLEY_RESPONSES),\n",
    "                \"intent\": xr.DataArray([intent] * N_TROLLEY_RESPONSES),\n",
    "                \"contact\": xr.DataArray([contact] * N_TROLLEY_RESPONSES),\n",
    "            }\n",
    "        )\n",
    "        ppd = pm.sample_posterior_predictive(\n",
    "            basic_ordered_logistic_inference, extend_inferencedata=False\n",
    "        )\n",
    "\n",
    "    # recode back to 1-7\n",
    "    posterior_predictive = ppd.posterior_predictive[\"response\"] + 1\n",
    "\n",
    "    # Posterior predictive density. Due to idiosyncracies with plt.hist(),\n",
    "    # az.plot_density() refuses to center on integer ticks, so we just do\n",
    "    # it manually with numpy and plt.bar()\n",
    "    if ax is not None:\n",
    "        plt.sca(ax)\n",
    "    else:\n",
    "        plt.subplots(figsize=(6, 3))\n",
    "    counts, bins = np.histogram(posterior_predictive.values.ravel(), bins=np.arange(1, 9))\n",
    "    total_counts = counts.sum()\n",
    "    counts = [c / total_counts for c in counts]\n",
    "    plt.bar(bins[:-1], counts, width=0.25)\n",
    "\n",
    "    plt.xlim([0.5, 7.5])\n",
    "    plt.xlabel(\"response\")\n",
    "    plt.ylabel(\"density\")\n",
    "    plt.title(f\"action={action}; intent={intent}; contact={contact}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "7db541ac-6d92-41ba-8ebc-38765efacc70",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Sampling: [response]\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "7559c0d7a0a34ca9a9d86a4d583ec58d",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 600x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "run_basic_trolley_counterfactual(action=0, intent=0, contact=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "c84c9e66",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Sampling: [response]\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "bf23e928683843b2a02de47cf7895cc9",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 600x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "run_basic_trolley_counterfactual(action=1, intent=1, contact=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "00abb839",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Sampling: [response]\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "0de220e6b83a461a84d073a2343a127c",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAmMAAAE3CAYAAADmP0YBAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjkuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8hTgPZAAAACXBIWXMAAA9hAAAPYQGoP6dpAABCjklEQVR4nO3deVhT19o28DtMKgYQcEYUlYJaReuAolit9TiiBRxrrVrBGcda6/hqrcdaexwK1Krt0bfOioK1KopDnXAAB0RFRSaVWBCZlIISIN8ffMlLmgibGNlB7t919brM3itrPXmSHZ7uvbK2RKFQKEBEREREojASOwAiIiKiqozFGBEREZGIWIwRERERiYjFGBEREZGIWIwRERERiYjFGBEREZGIWIwRERERiYjFGBEREZGIWIwRERERiYjFGJEOrly5AmdnZ3z++edih1KpOTs7w9nZWewwiIhEZSJ2AESGKCAgAAAwffp0kSOpeDdu3MDmzZtx48YN5ObmolGjRhg4cCB8fX1RrVo1scMrVXBwMGQyGby8vNCoUSOxw8Hdu3dx8uRJtGzZEr17937r48XFxeHy5cuIjo5GdHQ0kpKSoFAosHr1anzyySdvfXxDU9H5L01FfjaLiopw/vx51efg1q1byMzMhLGxMWJiYt7q2KQbFmNEWgQGBgJ4fTFWo0YNNG3aFA0aNKjIsN66Q4cOYf78+SgsLES9evXQoEEDPHjwAP7+/vjzzz+xfft21KhRQ2/jNW3aVG99AUBISAgiIiLg6upqMMVYYGAgvLy8KqQYWLt2LU6dOvXWx6ksKjr/panIz2ZOTg4mTpz4Vscg/WIxRqQDFxcXHDt2TOww9Co5ORmLFi1CYWEhvvrqK/j4+EAikUAmk8HHxwe3bt3CDz/8gP/5n//R25jvWg7FVq9ePfTt2xetW7eGi4sL1q1bh6ioKLHDogpmZGSEVq1aoU2bNnBxcUGdOnVYnBk4FmNEBAD473//i/z8fLi7u8PX11e13c7ODitXrsSnn36Kffv2YerUqahdu7aIkdLrLF26VO2xmZmZSJGQmKRSKUJCQlSPk5OTRYyGhGAxRpVKbGwsjh07hvDwcMhkMmRlZaFWrVpo164dxo8fj/bt27/2uampqdiyZQvOnz+PJ0+ewNjYGA0bNoS7uztGjBgBBwcHBAQEqC5RAtCYXH7q1Ck0atQIV65cwZgxY+Dq6ort27drjPXkyRNs2rQJ58+fx9OnT1GzZk20adMGn3/+OXr06KHRXjmun58fxo0bB39/f5w4cQLPnj1DgwYN4OnpiUmTJsHE5O0csgqFAidOnAAADBkyRGN/+/bt0axZMyQkJODUqVMYMWKEXsZV5vf+/ftq23v16gWZTIZTp07h2bNnCAwMxM2bNyGXy9GqVStMnz4dbm5uqvbK90Op5L8B4LvvvoO3t7fqcV5eHrZv345jx44hKSkJhYWFcHBwwKBBgzBmzBiNIkaX90f5GoDiS1Ql/zi+7nNTWQk5tkqqiONDaP7T0tJw7NgxnDlzBomJiUhLS0P16tXh7OyMoUOHwtPT87WvOy8vD7t27cLx48eRkJAAuVyOevXqoW3bthg2bBhcXV3L/dmkqonFGFUqK1euxKVLl2BpaYk6deqgbt26ePLkCU6cOIHTp0/j+++/x6BBgzSed+nSJfj5+SEnJwempqZo1qwZFAoFHj9+jC1btsDc3BzTp09HgwYN0L59e1y/fh0ANIo7IRPYb968CV9fXzx//hzm5uZwcnJCeno6zp8/j/Pnz2Pq1KmYOXOm1ue+ePECI0aMwMOHD/Hee+/ByMgIjx49gr+/P/766y+sWLFCh6yV7cmTJ0hLSwMAdOjQQWub9u3bIyEhATdv3lQrxlJSUjB8+HAAwL59+1C/fn29xXXmzBmsWrUKNWvWhL29PR49eoRr167B19cXW7ZsQefOnQEAFhYWaN++PWJjY5GTkwMnJydIpVJVP7a2tqp/p6amYvz48YiLi4OJiQns7OxgYmKCuLg4/PDDDzh9+jS2bNmC6tWra8RTnvendevWMDU1RVJSEmxtbdGkSRPVPicnJ73lSB9CQ0Px3XffoUGDBti7d2+5niv02FKqqONDaP6DgoLw448/onr16qhbty6cnJyQkZGByMhIREZG4saNG/jmm280Ynny5Al8fX0RHx8PAHBwcEDNmjUhk8lw6NAhpKSkYPv27eX6bFLVxWKMKpWRI0diwYIFamesFAoFTp06ha+++grLli3DRx99pPZl9+TJE0yfPh05OTnw9PTEggULUKtWLQDFvzo6d+4cioqKAABDhw7F0KFDVf3v3r27XPHl5eVh1qxZeP78Ofr3748VK1aoYgkJCcGiRYuwYcMGtGvXTusZgF27dqFdu3bYunUr6tWrBwA4ffo0/Pz8EBQUhC+++ALNmzdXtU9LS8OMGTPKFSMAtGrVCkuWLFE9fvjwIYDiy1p169bV+hx7e3u1tkoFBQVITU1V/VufVq1ahRkzZsDHxwfGxsaQy+VYsGAB/vjjD6xZswb79u1TvZ7du3fj888/R0REBBYvXqwq1EoqKirCzJkzERcXh4EDB2LhwoWqS64pKSn48ssvcfXqVfz444/4+uuvNZ5fnvfH398fwcHBWLBgAT788EOsWrVK62s8e/YsNm7cWO7cDBkyBEOHDi3387TJy8tDampquc+8lufYUo5TUceH0Py7urrit99+Q6dOnWBsbKzafu/ePcyZMwd79uzBwIED4erqqtpXWFgIPz8/xMfHo3Xr1li9erXacXn37l3cuHEDgPDPJgB8++23Ov3a0d/fH3Xq1Cn388hwsBijSqVfv34a2yQSCXr37o2xY8fi559/xp9//ql2duyXX37Bixcv4ObmhlWrVkEikaj2GRkZoWfPnnqL7/Dhw3jy5Alq166N77//Xu1MmpeXF6Kjo7Fr1y5s3rxZ6x8bY2Nj/Oc//1H9oQGKL7d8/PHHCAsLw/nz59W+9F+9eqU6i1ce//yjm52dDQCwtLRUy09JlpaWAIDnz5+Xezxdubu7q008NjU1xcKFC3H8+HHcvHkT2dnZsLKyEtzfmTNncOPGDbRp0warV69Wy0P9+vWxbt069O3bF3v27MHMmTM1zo6V9/0RIj09Xaf3sGvXruV+jr6V99iq6ONDiI4dO2rd3qJFCyxZsgTjxo3DH3/8oVaMhYWF4c6dO7C1tcWvv/4Ka2trtee2bNkSLVu2LFccQPE0DF0+C69evSr3c8iwsBijSufJkyc4fPgw7ty5g8zMTMjlcgBARkYGgOL/oy1ZjCl/6q/8deDbdOHCBQDAsGHDtF7SHDNmDHbt2qVaw8vc3Fxtf/fu3bVe5mvTpg3CwsLw+PFjte2NGjXSmG+lC+WXuamp6WvbKOdRvXz58q3EoM2wYcM0ttnY2MDOzg6JiYl4/PhxuYox5bw4Ly8vrWeB6tatizZt2uDKlSu4ffu2xh/q8r4/Qnh7e4s+Z0jXGMp7bFX08SFUTk4Ojh49imvXriEtLQ0vX76EQqFAfn4+gOLvlJKUr3vIkCEahdibeJfmEVL5sBijSiUkJARLly4t9f8ElWd5gOIvWeUltHbt2r3t8JCUlAQAcHR01LrfwcEBpqamkMvlePToEVq0aKG2X3kp8J9sbGwAALm5ufoLtgTlH0ZlYauN8g+TtrlUb0vjxo21bre1tUViYmK586EsGvfs2YPDhw9rbaN8D58+faqxT6z3xxDpcmwZ4vERExODSZMmaX2/lUp+pwBAQkICAKBt27blHo9IGxZjVGk8evQIS5YsgVwux/jx4zF48GDY29ujZs2akEgkCAoKwuLFi9XmLf3999+qf1tYWLz1GJV/DF43KVcikcDGxgapqalqsSn980yAkpFR8Z3LFAqFniJVpzy79Pz5cygUCq1nOZSXJ5WXKyvC6xaY1TUfOTk5AIovB5Xln2cAAfHeH0Oky7FlaMdHYWEhZs2ahadPn6JHjx6YMGECHB0dYWlpCWNjYzx8+BB9+vTRmAup/BxV5LFA7zYWY1RphIaGQi6XY+DAgVonV//1118a22rWrKn694sXL956Qab8Y5Genq51v0KhUF1OLRmbrvQ1gV/5S7P8/Hw8ffpUbU6OkvISUMlfpVU2yvdn69atBjHnCjCMCfy60OXYqujjoyzR0dF4+PAh7OzsEBgYqLGkibbvlJKx6Xv+JCfwV10sxqjSUK4Z9MEHH2jd/895HUDx4of169dHSkoKoqKi0L1797cao4ODA+7du4e4uDit+5OSkiCXy2FsbPzaS3Dloa8J/A0bNkSdOnWQlpaGa9euYcCAARrPUY5TmS/NNG/eHHfv3kVsbGyFFGNC5lFV1gn8uhxbFX18lJV/5XfK+++/r3WBXG3fKUDxZdaYmBjcvHlTr7dZ4gT+qovFGFUaynlNz54909gXHx+PP//8U+vzevfujR07dmDr1q2Ci7Hq1avj5cuXePnyZbnmSLm7u+PYsWMICgrClClTNCYpKyfotm/f/rWXXMpDX5Pnlb9I3b17Nw4cOKBRjF2/fh0JCQkwNTVFr1693ni8t0X5Xmm7xAgAffr0weHDh7F37158+umnb/3G58r+XxcPYBgT+HVV3mOroo+PsvKv/Lxo+06Ry+XYtm2b1uf17t0bhw4dwoEDB+Dj46NazqM0ZX02AU7gr8qMxA6ASCjlYqS7d+/G3bt3VdsTExMxa9as1/4S0MfHBxYWFggPD8fChQvVJuMWFRXh7NmzGoWccqJwREREuWL08PBAw4YN8ezZM8yfP19t3svvv/+uWlBzwoQJ5eq3Ivj4+MDU1BQXLlzAr7/+qpp/I5PJsHDhQgDF67D983JIcnIynJ2d4ezsLPptV5TvW2RkpNb9//rXv9CuXTskJCRg8uTJGmum5efn48yZM1iwYIFe47l16xby8vL00ufbEBwcDGdn53IX2uU9tir6+Cgr/23btoWJiQmuX7+OgwcPqra/ePECc+fO1VqkAcXFWOvWrZGeno6JEyeqJvQr3bt3D7t27dIay+s+m1S18cwYVRq9e/dGu3btEBUVhSFDhsDBwQHGxsZ48OABateujSlTpmD9+vUaz2vYsCH8/f3h5+eHAwcO4NChQ2jevDmKioqQnJyM3Nxc+Pn54aOPPlI9p3///njw4AEmT54MZ2dn1cKUa9euLXVuRo0aNbB+/Xr4+Pjg6NGjOHPmDJo1a4b09HTV/JMpU6ZoXUNJbPb29lixYgUWLFiAH374Adu2bYOtrS0ePHgAuVyO999/H/PmzRM7zFINGDAAO3fuxC+//IITJ06gTp06kEgkmDBhAj788EMYGRkhICAAkyZNwsWLF9GnTx80adIEtWrVwt9//42HDx9CLpfr7d6b77//PhwcHJCUlISePXuiadOmMDU1RYsWLbBo0SK9jFHSkSNHsHz5ctVj5UTzZcuWYeXKlartV65c0ct45T22Kvr4KCv/derUwZgxY7BlyxZ8/fXX+PHHH2FtbY34+HgUFhZi0aJFWLZsmUa/xsbGCAgIwPjx43Hz5k30799fbQX+rKwsuLq6YtSoUarnlPXZ1LcpU6aoLnkq/8eqsLBQbcFZDw8PtbmjJB4WY1RpmJiY4L///S/Wr1+P48eP49GjR7C1tcXQoUMxY8YM1RpG2nTt2hWHDx/Gf//7X5w/fx4JCQmoXr067O3t4e7ujk8++USt/cSJE1FUVIQjR44gLi5OtayDkLkZbdu2xe+//45NmzbhwoULuH//PszNzeHu7o4xY8YYZCGm5OnpicaNG2Pz5s24ceMG4uLi0KhRI3h4eGDChAlaL+spzx7Y2tq+dvX+itKxY0esWbMGv/32G+Li4lRLKXh5eana1K1bF3v37sX+/ftx9OhRxMbGqhYidXFxQdeuXdG/f3+9xGNkZIRNmzZh7dq1uHr1KqKjo1FYWKiXvrV59eoVsrKyNLbn5uaWuuyD8j3851ISQpT32KrI40NI/ufNm4f69etjz549ePz4MfLy8uDm5oYpU6aUequihg0bIjg4GDt27MCxY8eQmJgIhUKBevXq4aOPPtL4cYWQz6Y+5eTkaP0slNym7RerJA6Joir9FpuI9G7r1q1YtWoVpk+fDj8/P7HDIR1MnjwZf/75J3bs2IFOnTqJHQ5RlcM5Y0T0Rq5fv47q1aurXZKhyiUqKgqtW7dmIUYkEhZjRPRGbty4gU8++US1CjpVLgkJCcjMzMQXX3whdihEVRYvUxIRERGJiGfGiIiIiETEYoyIiIhIRCzGiIiIiETEYoyIiIhIRCzG3mEKhQJZWVngbzRKxzwJwzwJwzwJwzwJwzwJV5lzxWLsHVcZP5RiYJ6EYZ6EYZ6EYZ6EYZ6Eq6y5YjFGREREJCIWY0REREQiYjFGREREJCIWY0REREQiYjFGREREJCIWY0REREQiYjFGREREJCIWY0REREQiMhE7ANJdtM0AQe0el7HfJePomwdDREREOuGZMSIiIiIRsRgjIiIiEhGLMSIiIiIRsRgjIiIiEhGLMSIiIiIRsRgjIiIiEhGLMSIiIiIRcZ0xIqJy4Pp+RKRvPDNGREREJCIWY0REREQiYjFGREREJCIWY0REREQiYjFGREREJCIWY0REREQiYjFGREREJCIWY0REREQiYjFGREREJCIWY0REREQiYjFGREREJCIWY0REREQiYjFGREREJCIWY0REREQiYjFGREREJCIWY0REREQiYjFGREREJCIWY0REREQiMhE7gNeJjo5GQEAAoqKiIJfL4ejoiLFjx2LQoEGCnn/16lWcPHkSERERkMlkyM3NhZ2dHT7++GNMmjQJlpaWb2VcIiIiovIwyGLsypUr8PHxgampKQYOHAgLCwuEhYVh7ty5kMlkmDx5cpl9zJw5E5mZmejQoQM++eQTSCQSRERE4Ndff0VYWBj27NkDW1tbvY9LREREVB4ShUKhEDuIkgoKCtC/f3+kpKRg7969aNWqFQAgJycHI0eORGJiIo4cOQIHB4dS+9m8eTM8PT1Rt25d1TaFQoFvvvkGu3fvxqhRo7B06VK9j1uRom0G6KUfl4yjeumnslIoFMjKykKtWrUgkUjEDsdgMU/FeNzpBz9PwjBPwlXmXBncnLHLly/j0aNH8PDwUBVEACCVSjF16lQUFBQgODi4zH4mTpyoVogBgEQiwdSpUwEAkZGRb2VcIiIiovIwuGIsIiICAODu7q6xr1u3bmptdGFiUnxl1tjYuELHJSIiItLG4OaMJSUlAQCaNGmisc/KygrW1tZ4+PChzv0fOHAAwP8VWPoc18Cu+ApWWePWF+Xrr+p5KAvzpF9VPY/8PAnDPAlnqLkScsnU4IqxnJwcAICFhYXW/VKpFCkpKTr1fffuXfz000+wtbWFr6+v3sfNzs42uA+BEFlZWWKHYBCys7PFDqFSYJ70g8ddMX6ehGGehDO0XFlbW5fZxuCKsbfl8ePHmDRpEgoLC7F27VrY2NjofQwrKyu991max3rqp1atWnrqqXJSKBTIzs6GlZVVpZv0WZGYp2I87vSDnydhmCfhKnOuDK4Yk0qlAIAXL15o3Z+Tk/Pas1evI5PJMHbsWGRkZCAgIABdunR5K+NWtjdfqbLGrW8SiYS5EIB50g/msBg/T8IwT8JVxlwZ3AR+5dIR2uZnZWdnIzMzU+u8rtdJTk7G559/jqdPn2L9+vX46KOPKmRcIiIiIiEMrhjr1KkTAODChQsa+8LDwwEArq6ugvpKTk7GmDFj8PTpU6xbtw69e/eukHGJiIiIhDK4YszNzQ329vY4fPgw7t69q9qek5ODDRs2wMTEBF5eXqrtGRkZiI+PR0ZGhlo/ykIsNTUVa9euxb/+9S+9jktERESkDwY3Z8zExAQrVqyAr68vRo0aBQ8PD0ilUoSFhSE5ORmzZs1C06ZNVe137tyJwMBA+Pn5Yfr06artY8aMgUwmQ7t27XD//n3cv39fY6yS7cs7LhEREZE+GFwxBgBdunTBrl274O/vj9DQUNUNu2fOnInBgwcL6kMmkwEAoqKiEBUVpbVNyWJMX+MSERERlYfB3ZuShOM98vSjMt/PrCIxT8V43OkHP0/CME/CVeZcGdycMSIiIqKqhMUYERERkYhYjBERERGJiMUYERERkYhYjBERERGJiMUYERERkYhYjBERERGJiMUYERERkYhYjBERERGJiMUYERERkYhYjBERERGJiMUYERERkYhYjBERERGJiMUYERERkYhYjBERERGJiMUYERERkYhYjBERERGJiMUYERERkYhYjBERERGJiMUYERERkYhYjBERERGJiMUYERERkYhYjBERERGJiMUYERERkYh0LsZWrlyJuLg4fcZCREREVOXoXIxt27YNgwYNwsiRIxEcHIy8vDx9xkVERERUJehcjG3atAkff/wxbt++jUWLFsHd3R1Lly7F7du39RkfERER0TvNRNcn9ujRAz169EBGRgaCg4Nx4MAB7N27F/v27YOzszOGDx+OwYMHQyqV6jNeIiIionfKG0/gt7Gxga+vL0JDQ7Fjxw588sknePjwIb799lu4u7tj/vz5uHbtmj5iJSIiInrn6HxmTJuOHTuiY8eOGDJkCL788ks8ffoUBw8exO+//w5HR0dMmzYN/fr1E9RXdHQ0AgICEBUVBblcDkdHR4wdOxaDBg0S9Pz09HTs378fd+7cwe3btyGTyQAA9+/ff+1zevXqpWr3TyNGjMDy5csFjU1EREQklN6KsZycHPzxxx8ICgrC3bt3oVAo8MEHH8DLywsxMTH4/fffMXv2bDx+/BgTJkwota8rV67Ax8cHpqamGDhwICwsLBAWFoa5c+dCJpNh8uTJZcYTFxeHtWvXQiKRoEmTJqhRo4agHxlYWFhg7NixGttbt25d5nOJiIiIykuiUCgUb9LB1atXsX//fhw/fhx5eXmwtLTE4MGDMWLECLz33nuqdhkZGRg3bhyys7Nx9uzZ1/ZXUFCA/v37IyUlBXv37kWrVq0AFBd7I0eORGJiIo4cOQIHB4dS43r27BkSExPRsmVLSKVS9OvXD4mJiWWeGQOA06dPlyMD4om2GaCXflwyjuqln8pKoVAgKysLtWrVgkQiETscg8U8FeNxpx/8PAnDPAlXmXOl85mxLVu2ICgoCElJSVAoFGjXrh1GjBiBAQMGoFq1ahrtbWxs0LdvXwQGBpba7+XLl/Ho0SN4e3urCjEAkEqlmDp1KmbPno3g4GDMmTOn1H5q166N2rVr6/biiIiIiCqIzsXY6tWrYWFhgVGjRmHEiBFwcnIq8zmtW7eGp6dnqW0iIiIAAO7u7hr7unXrptbmbcjPz0dISAhSU1NhaWmJ9u3bo0WLFoKe+4YnGUVTWePWF+Xrr+p5KAvzpF9VPY/8PAnDPAlnqLkScpZO52Js5cqVGDBgAKpXry74OcrlMEqTlJQEAGjSpInGPisrK1hbW+Phw4flirU80tLSMH/+fLVt3bt3x+rVq2FjY1Pqc7Ozsw3uQyBEVlaW2CEYhOzsbLFDqBSYJ/3gcVeMnydhmCfhDC1X1tbWZbbRuRgzMjJCUlJSqWeNYmNjERMTU+bZsJJycnIAFE+k10YqlSIlJaVcsQrl7e0NV1dXODo6wszMDPHx8QgMDMS5c+cwdepU7N69u9QK18rK6q3E9TqP9dRPrVq19NRT5aRQKJCdnQ0rK6tKN8+gIjFPxXjc6Qc/T8IwT8JV5lzpXIwtWLAA06ZNK7UYO3PmDNatW1euYkxMfn5+ao/btm2LTZs2YfTo0bh27RrOnj2Lnj17vvb5le3NV6qsceubRCJhLgRgnvSDOSzGz5MwzJNwlTFXOi/6KuRyXFFREYyMyjeEcsX+Fy9eaN2fk5Pz2rNmb4ORkRG8vb0BANevX6+wcYmIiKhqeOMV+EsTExNT7kt3yiUrtM0Ly87ORmZmptb5ZG+T8novb4ZORERE+lauy5RjxoxRexwSEqL1l41FRUVISUmBTCZD//79yxVQp06dsGnTJly4cAEDBw5U2xceHg4AcHV1LVefbyo6OhoAYGdnV6HjEhER0buvXMVYycJLIpFAJpNpvX2QkZERrKys0K9fPyxcuLBcAbm5ucHe3h6HDx/GmDFj0LJlSwDFlyc3bNgAExMTeHl5qdpnZGQgMzMT1tbWZf7asTRxcXGoW7cuLC0t1bZfvXoVW7duhZmZGfr06aNz/yQeoYt0ljUxu6ov0klERG9HuYqxe/fuqf7dokUL+Pn5aUx6f+OATEywYsUK+Pr6YtSoUfDw8IBUKkVYWBiSk5Mxa9YsNG3aVNV+586dCAwMhJ+fH6ZPn67WV8klKtLS0jS2zZs3T1XAhYaG4tdff4Wbmxvs7OxgZmaG2NhYhIeHw8jICN988w0aNmyo19dKZEhYtBIRiUPnX1Nu27btrV2269KlC3bt2gV/f3+EhoaqbhQ+c+ZMDB48WHA/ISEhpW7z8/NTFWOdO3dGfHw8YmJiEBERgfz8fNja2mLAgAEYN24cXFxc3vyFEREREf3DG9+bksTDe+QJwzwJwzwJwzzpR2W+j2BFYp6Eq8y5Enxm7ODBgwCA3r17QyqVqh4LUVnWGSMiIiKqaIKLsfnz50MikaBt27aQSqWqx6VRKBSQSCQsxoiIiIheQ3AxtnLlSkgkEtSpUwcA8N133721oIiIiIiqCsHFmHIVeqWSy0sQERERkW7e6gr8RERERFQ6nYux1NRUREZGqt0iqKioCJs3b8bIkSPxxRdf4Ny5c3oJkoiIiOhdpfM6Y+vXr8epU6dUtygCgJ9//hkBAQGqx5GRkdi9ezfatGnzZlESERERvaN0PjMWFRUFNzc3mJqaAig+K7Zjxw40a9YMZ86cQVBQEKpXr44tW7boLVgiIiKid43OxVhaWpraCvx37txBZmYmRo8ejfr166NNmzbo3bs3bt68qZdAiYiIiN5FOhdjhYWFKLl4/7Vr1yCRSNClSxfVtnr16uHZs2dvFiERERHRO0znYqxhw4aIjo5WPT558iTq1KmDZs2aqbalpaXB0tLyzSIkIiIieofpPIG/T58+2LhxI2bMmIFq1arh2rVr+Oyzz9TaPHjwAI0aNXrjIImIiIjeVToXYz4+PggPD0dYWBgAwMnJCX5+fqr98fHxuHXrFiZNmvTmURIRERG9o3QuxqRSKfbt24fY2FgAQPPmzWFsbKzaX61aNQQGBnJZCyIiIqJS6FyMKTk5OWnd3qhRI16iJCIiIioDb4dEREREJKI3OjN28eJFbN26Fbdu3cKLFy9QVFSk0UYikSAmJuZNhiEiIiJ6Z+lcjB0/fhyzZ89GUVERGjZsiGbNmqnNGSMiIiKisulcjP3000+oVq0aNmzYADc3N33GRERERFRl6DxnLDExEQMHDmQhRkRERPQGdC7GrK2tUb16dX3GQkRERFTl6FyM9e/fH5cuXUJBQYE+4yEiIiKqUnQuxmbNmgVLS0vMnj0bT5480WdMRERERFWGzhP4PTw8UFBQgKioKJw8eRKWlpaQSqUa7SQSCU6ePPlGQRIRERG9q3QuxhQKBYyNjdGgQQO1bdraEREREZF2Ohdjp0+f1mccRERERFUSb4dEREREJKI3vlE4AMTFxSEhIQG5ubnw9PTUR5dEREREVcIbFWPR0dFYsmQJYmNjVduUxVhkZCR8fX2xdu1afPzxxzr1HRAQgKioKMjlcjg6OmLs2LEYNGiQoOenp6dj//79uHPnDm7fvg2ZTAYAuH///lsdl4iIiKg8dC7GHjx4gLFjx8LIyAjjxo1DQkICzp07p9rfsWNHWFtb49ixY+Uuxq5cuQIfHx+Ymppi4MCBsLCwQFhYGObOnQuZTIbJkyeX2UdcXBzWrl0LiUSCJk2aoEaNGsjLy3vr4xIRERGVh87FWEBAAAAgODgYTZo0QWBgoFoxJpFI0K5dO9y6datc/RYUFGDx4sWQSCTYuXMnWrVqBQCYNm0aRo4ciYCAAPTr1w8ODg6l9tO8eXPs2LEDLVu2hFQqRb9+/ZCYmPjWxyUiIiIqD52LsYiICPTt2xdNmjR5bZsGDRrg/Pnz5er38uXLePToEby9vVUFEQBIpVJMnToVs2fPRnBwMObMmVNqP7Vr10bt2rUrfFwiIgKibQYIavdYQBuXjKNvFgyRgdP515R///03bGxsSm2Tn5+PoqKicvUbEREBAHB3d9fY161bN7U2+iTWuERERFS16XxmrEGDBnjw4EGpbe7cuQN7e/ty9ZuUlAQAWs+4WVlZwdraGg8fPixXnxU1bmVd4Layxl3RmCdhmCdhmCfhqnKulK+9KudAKEPNlUQiKbONzsVYz549sWPHDly6dAlubm4a+48ePYqoqChMnTq1XP3m5OQAACwsLLTul0qlSElJKX/AFTBudna2wX0IhMjKyhI7hEqBeRKGeRKGeRKOuSr++0LCGFqurK2ty2yjczE2efJkHD9+HBMmTICXlxfS0tIAADt37kRUVBSOHDkCOzs7fPHFF7oOUelYWVlV6HhC5loIUatWLT31ZJiYJ2GYJ2GYJ2H0lSfg3c9VaRQKBbKzs2FlZSXoDEtVVplzpXMxZmNjgx07duCrr75CUFCQavu3334LAGjbti3WrFnz2jNNr6O82fiLFy+07s/JySl3nxU1bmV785Uqa9wVjXkShnkShnkSjrkqzgHzIExlzNUbLfpqb2+PPXv2ICYmBjdv3kR2djakUilcXFzg4uKiU5/KpSMePnyI1q1bq+3Lzs5GZmYmPvjggzcJ26DGJSIioqpNL/embNWqFT799FNMnjwZo0eP1rkQA4BOnToBAC5cuKCxLzw8HADg6uqqc/+GNi4RERFVbYLPjC1YsECnASQSCVauXCm4vZubG+zt7XH48GGMGTMGLVu2BFB8mXDDhg0wMTGBl5eXqn1GRgYyMzNhbW1d5lIb+hyXiIiISB8EF2MhISFat0skEq2/IFRuL28xZmJighUrVsDX1xejRo2Ch4cHpFIpwsLCkJycjFmzZqFp06aq9jt37kRgYCD8/Pwwffp0tb7mz5+v+rfyBwYlt82bN09VwJV3XCIiIiJ9EFyMnTp1Su1xUVER/v3vf+PmzZsYM2YMOnbsCFtbW6SnpyMyMhLbt29Hu3btsHDhwnIH1aVLF+zatQv+/v4IDQ1V3bB75syZGDx4sOB+tBWQJbf5+fmpnU3T17hEREREQkkUOi6MtXnzZvzv//4vDh48iLp162rsT01NhaenJ8aPH48JEya8caCkSejtRsryrt9qhHkShnkShnkSRl95At79XJVGoVAgKysLtWrVqnS/EKxolTlXOk/g379/P/r376+1EAOAevXqoX///mrLXhARERGROp2LsZSUFJiZmZXaplq1am9ltXwiIiKid4XOxVj9+vVx8uRJvHr1Suv+vLw8nDhxAvXr19c5OCIiIqJ3nc7F2NChQ/H48WN8+umnOHnyJDIzMwEAmZmZOHnyJEaNGgWZTIZhw4bpLVgiIiKid43OK/D7+voiKSkJwcHBqiUljIyMUFRUBKB4Ip23tzd8fX31EykRERHRO0jnYszIyAgrV66Ep6cnQkJCcP/+feTk5EAqlaJFixbw9PTkivVEREREZXije1MCxbcIYtFFREREpBu93JuSiIiIiHTzxmfGiIiISDdCF8d9LKBNVV4ct7LjmTEiIiIiEbEYIyIiIhIRizEiIiIiEbEYIyIiIhIRizEiIiIiEbEYIyIiIhIRizEiIiIiEbEYIyIiIhIRizEiIiIiEbEYIyIiIhIRizEiIiIiEbEYIyIiIhIRizEiIiIiEZmIHQARERFRaaJtBghu+7iM/S4ZR98smLeAZ8aIiIiIRMRijIiIiEhELMaIiIiIRMRijIiIiEhELMaIiIiIRGSwv6aMjo5GQEAAoqKiIJfL4ejoiLFjx2LQoEGC+ygqKsKuXbuwd+9ePHz4EObm5ujcuTNmz54NBwcHjfa9evWCTCbT2teIESOwfPlyXV8OERERkVYGWYxduXIFPj4+MDU1xcCBA2FhYYGwsDDMnTsXMpkMkydPFtTP0qVLsW/fPjg6OmL06NFIT0/H0aNHER4ejj179sDR0VHjORYWFhg7dqzG9tatW7/x6yIiIiL6J4MrxgoKCrB48WJIJBLs3LkTrVq1AgBMmzYNI0eOREBAAPr166f1zFZJly9fxr59+9CxY0ds3boVZmZmAABPT0988cUXWLZsGXbs2KHxPEtLS0yfPl3vr4uIiIhIG4ObM3b58mU8evQIHh4eqkIMAKRSKaZOnYqCggIEBweX2U9QUBAAYNasWapCDADc3Nzg7u6OyMhIJCYm6v8FEBEREZWDwZ0Zi4iIAAC4u7tr7OvWrZtam9JcuXIF5ubmaN++vcY+d3d3nD9/HpGRkWjatKnavvz8fISEhCA1NRWWlpZo3749WrRooctLISIiIiqTwRVjSUlJAIAmTZpo7LOysoK1tTUePnxYah+5ublIS0uDk5MTjI2NNfYrL3EqxyopLS0N8+fPV9vWvXt3rF69GjY2NqWOq1AoSt1vqCpr3BWNeRKGeRKGeRKOuRKGeRKmovMkkUjKbGNwxVhOTg6A4on02kilUqSkpJTax4sXL1RtX9dHybGUvL294erqCkdHR5iZmSE+Ph6BgYE4d+4cpk6dit27d5ea1Ozs7Ep5MGRlZYkdQqXAPAnDPAnDPAnHXAnDPAlT0XmytrYus43BFWNi8vPzU3vctm1bbNq0CaNHj8a1a9dw9uxZ9OzZ87XPt7KyessRqivrZqhC1apVS089GSbmSRjmSRjmSRh95Ql4t3PFPAnzrufJ4Iox5Vkr5dmtf8rJyXntWTMl5f5/nvkq2UfJsUpjZGQEb29vXLt2DdevXy+1GBNyKtIQVda4KxrzJAzzJAzzJBxzJQzzJIwh5sngfk2pnM+lbV5YdnY2MjMztc4nK8nc3Bx16tRBcnIyCgsLNfYr54qVtTyGkvIUY15enqD2REREREIZXDHWqVMnAMCFCxc09oWHhwMAXF1dy+zH1dUVubm5uH79usY+Zd/KscoSHR0NALCzsxPUnoiIiEgogyvG3NzcYG9vj8OHD+Pu3buq7Tk5OdiwYQNMTEzg5eWl2p6RkYH4+HhkZGSo9TN8+HAAwPr165Gfn6/afunSJVy4cAGdOnVSW9YiLi4Oz58/14jn6tWrqkVj+/Tpo7fXSURERAQY4JwxExMTrFixAr6+vhg1ahQ8PDwglUoRFhaG5ORkzJo1S62I2rlzJwIDA+Hn56e2cn6XLl0wbNgwBAUFwcvLCz169FDdDkkqlWLZsmVq44aGhuLXX3+Fm5sb7OzsYGZmhtjYWISHh8PIyAjffPMNGjZsWFFpICIioirC4IoxoLiQ2rVrF/z9/REaGqq6UfjMmTMxePBgwf0sX74czs7O2Lt3L7Zv3w5zc3N89NFHmD17tsZir507d0Z8fDxiYmIQERGB/Px82NraYsCAARg3bhxcXFz0/TKJiIiIIFFUxoWxCAAQbTNAL/24ZBzVSz+GinkShnkShnkSRl95At7tXDFPwrzreTK4OWNEREREVQmLMSIiIiIRsRgjIiIiEhGLMSIiIiIRsRgjIiIiEhGLMSIiIiIRsRgjIiIiEhGLMSIiIiIRsRgjIiIiEhGLMSIiIiIRsRgjIiIiEhGLMSIiIiIRsRgjIiIiEhGLMSIiIiIRsRgjIiIiEhGLMSIiIiIRsRgjIiIiEhGLMSIiIiIRsRgjIiIiEhGLMSIiIiIRsRgjIiIiEhGLMSIiIiIRsRgjIiIiEhGLMSIiIiIRsRgjIiIiEhGLMSIiIiIRsRgjIiIiEhGLMSIiIiIRsRgjIiIiEpHBFmPR0dGYMGECOnXqhHbt2mHo0KH4448/ytVHUVERduzYgUGDBsHFxQVdunTBzJkzkZSU9FbHJSIiIhLKROwAtLly5Qp8fHxgamqKgQMHwsLCAmFhYZg7dy5kMhkmT54sqJ+lS5di3759cHR0xOjRo5Geno6jR48iPDwce/bsgaOj41sZl4iIiEgogyvGCgoKsHjxYkgkEuzcuROtWrUCAEybNg0jR45EQEAA+vXrBwcHh1L7uXz5Mvbt24eOHTti69atMDMzAwB4enriiy++wLJly7Bjxw69j0tERERUHgZ3mfLy5ct49OgRPDw8VAURAEilUkydOhUFBQUIDg4us5+goCAAwKxZs1SFGAC4ubnB3d0dkZGRSExM1Pu4REREROVhcMVYREQEAMDd3V1jX7du3dTalObKlSswNzdH+/btNfYp+46MjNT7uERERETlYXCXKZWT65s0aaKxz8rKCtbW1nj48GGpfeTm5iItLQ1OTk4wNjbW2K+81FhyIr8+xlUoFKXuN1SVNe6KxjwJwzwJwzwJx1wJwzwJU9F5kkgkZbYxuGIsJycHAGBhYaF1v1QqRUpKSql9vHjxQtX2dX2UHEtf4wpJuD65ZByt0PEqK+ZJGOZJGOZJGOZJGOZJmHc9TwZ3mZKIiIioKjG4Ykx51kp5duufcnJyXnv2Skm5v+SZr3/2UXIsfY1LREREVF4GV4wp53Npm5+VnZ2NzMxMrfO6SjI3N0edOnWQnJyMwsJCjf3K+WEll6nQx7hERERE5WVwxVinTp0AABcuXNDYFx4eDgBwdXUtsx9XV1fk5ubi+vXrGvuUfSvH0ue4REREROVhcMWYm5sb7O3tcfjwYdy9e1e1PScnBxs2bICJiQm8vLxU2zMyMhAfH4+MjAy1foYPHw4AWL9+PfLz81XbL126hAsXLqBTp05o2rSpzuMSERER6YNEYYC/hb18+TJ8fX1hamoKDw8PSKVShIWFITk5GbNmzcKUKVNUbQMCAhAYGAg/Pz9Mnz5drZ/FixcjKCgIjo6O6NGjh+p2SNWqVdN6O6TyjGuofv/9d1y7dg23b99GbGws5HI5vvvuO3h7e4sdmsFITU1FaGgozp07h4SEBDx79gxWVlZo3749fH190bZtW7FDNAjPnz+Hv78/bt26heTkZGRnZ8Pa2hpNmzbFZ599hj59+lT4L4gri19++QX/+c9/AAB79+5Fu3btxA3IgPTq1QsymUzrvhEjRmD58uUVHJFhO3HiBHbt2oWYmBjk5eWhdu3aaNeuHb766is0aNBA7PBEFRwcjAULFpTapkuXLvjtt98qKCLdGdzSFkBx8nbt2gV/f3+EhoZCLpfD0dERM2fOxODBgwX3s3z5cjg7O2Pv3r3Yvn07zM3N8dFHH2H27NlqZ8X0Pa6YfvzxR8hkMlhbW6Nu3bqv/dKryrZv345ffvkFjRs3RteuXWFra4uHDx/i5MmTOHnyJNasWYMBAwaIHaboMjMzceDAAbRt2xYff/wxatWqhfT0dPz555+YMWMGhg8fjm+//VbsMA1OfHw8/P39YW5ujtzcXLHDMUgWFhYYO3asxvbWrVuLEI1hUigUWLp0Kfbu3YvGjRtjwIABqFmzJp4+fYrIyEjIZLIqX4y1bNkSfn5+WvcdP34cDx480LqQuyEyyDNjpLuLFy+iSZMmsLOzw+bNm7FmzRqeGfuHsLAw2NjYoGPHjmrbr169inHjxqFmzZo4f/682m20qqLCwkIoFAqYmKj/P1tOTg5GjBiBuLg4HD58GO+9955IERqewsJCjBgxAhKJBA4ODjh06BDPjP1Dr169AACnT58WORLDtm3bNvz73//GZ599hkWLFmksYF5QUKBxbFKx/Px8dO/eHTk5OTh79ixq164tdkhlMrg5Y/RmunbtCjs7O7HDMGh9+vTRKMQAoGPHjujcuTOysrJw//59ESIzLMbGxlq/7KVSqer/Nsu6K0VV88svv+DevXtYuXKl1rt/EAnx8uVL/PTTT7C3t8fChQu1fpZYiL3eyZMnkZWVhZ49e1aKQgww0MuURGJRfsHxi+71Xr16hcuXL0MikWjMu6zKYmNjERgYiClTpvBsYRny8/MREhKC1NRUWFpaon379mjRooXYYRmM8PBwZGVlwcvLC0VFRQgLC0NSUhIsLCzQtWtXLrNUhv379wMAhg0bJnIkwvEvDtH/9+TJE1y8eBF16tSBk5OT2OEYjOfPn+O3335DUVER0tPTce7cOfz111/w8/NTW6uvKisoKMD8+fPRvHlzTJw4UexwDF5aWhrmz5+vtq179+5YvXo1bGxsRIrKcNy+fRtA8dnpwYMHIzExUbXPyMgI48aNw9dffy1WeAZNJpPh0qVLqFevHrp37y52OIKxGCMCIJfLMW/ePOTn52Pu3Lm8xFTC8+fPERgYqHpsamqKefPmYfz48SJGZVg2btyI+/fvY9++fTA1NRU7HIPm7e0NV1dXODo6wszMDPHx8QgMDMS5c+cwdepU7N69u8r/Sjc9PR0AsHXrVrRq1QpBQUFo3rw57t69iyVLlmDLli2wt7fHqFGjRI7U8AQHB6OoqAje3t6V6nucc8aoyisqKsLChQsRGRmJ4cOHw9PTU+yQDEqjRo1w//59xMTE4NSpU5gxYwbWrVuH6dOno6CgQOzwRHfv3j1s3LgR48ePx/vvvy92OAbPz88Prq6usLGxgVQqRdu2bbFp0yZ06NABN27cwNmzZ8UOUXTK39WZmprip59+gouLC2rWrImOHTvC398fRkZG2Lp1q8hRGp6ioiIEBwdDIpFgyJAhYodTLizGqEpTKBRYvHgxDh06hMGDB+Obb74ROySDZWxsjEaNGmHixImYNWsWTpw4gX379okdlui+/vpr2Nvba6xzSMIZGRmpfvGt7a4pVY3yXsmtW7dGvXr11Pa99957sLe3x6NHj/D8+XMxwjNY4eHhePLkCbp06QJ7e3uxwykXFmNUZSnPiB04cAAeHh5YtWoVjIx4SAih/DVlRESEyJGI7969e0hISECbNm3g7Oys+i8kJARA8UKmzs7OOHnypMiRGjZra2sAQF5ensiRiK9Zs2YAitdj00a5/eXLlxUWU2VQGSfuK3HOGFVJRUVFWLRoEYKDgzFgwACsXr26Us0vEFtqaioAMGcAhg4dqnX71atXkZSUhF69esHGxoZLzpQhOjoaAJgnAJ07dwYAJCQkaOyTy+V49OgRzM3N+WOHEjIzM3Hq1CnUqlUL//rXv8QOp9xYjFGVU7IQ69evH3744QcWFVrcvXsXjRo10vi/86ysLKxbtw4A8OGHH4oRmkH597//rXX7/PnzkZSUhEmTJnHR1/8vLi4OdevWhaWlpdr2q1evYuvWrTAzM0OfPn1Eis5wNG7cGO7u7rhw4QKCgoLUzvRs3rwZz58/x+DBg7kETwm///475HI5Bg0aVCkX7OY7+Y4JCgrCtWvXABSve6Tcpryc1Lt3b/Tu3Vu0+AzBTz/9hODgYJibm8PBwQE///yzRpvevXujZcuWIkRnOIKDg7F//3507twZDRs2RI0aNfDkyROcOXMGubm56Nu3LwYNGiR2mFSJhIaG4tdff4Wbmxvs7OxgZmaG2NhYhIeHw8jICN988w0aNmwodpgGYenSpRg5ciQWL16MkydPolmzZoiJicHly5dhZ2eHefPmiR2iQTlw4ACAynmJEmAx9s65du2aaq6K0vXr11WTYu3s7Kp8Maa8X2dubi42btyotY2dnV2VL8b69u2LnJwcREVFITIyEi9fvoSVlRU6dOgAT09PDBw4sMovQUDl07lzZ8THxyMmJgYRERHIz8+Hra0tBgwYgHHjxsHFxUXsEA1G48aNceDAAfj7++P8+fMIDw9H7dq18dlnn2HatGmwtbUVO0SDER0djdjYWLi4uMDZ2VnscHTCe1MSERERiYg/HSMiIiISEYsxIiIiIhGxGCMiIiISEYsxIiIiIhGxGCMiIiISEYsxIiIiIhGxGCMiIiISEYsxIiIiIhGxGCMiIiISEYsxIiIiIhGxGCMiIiISEYsxIiIiIhGxGCMiIiISkYnYARARCXXlyhWMGTMGfn5+cHd3R2BgIG7evIkXL17g/v37UCgUOHDgAPbv34/Y2FgUFhaiefPmGDVqFIYOHarW16tXr7Bz504cPHgQMpkMRUVFsLW1hYuLCyZNmgRnZ2cAQHBwMBYsWIDvvvsOFhYW2LhxI+Li4iCVStG7d2/MmTMHVlZWGrFev34dGzduRFRUFPLy8mBnZ4eBAwfC19cXNWrUUGvr7OwMV1dX/Pjjj/jhhx9w5swZ/P3332jRogW+/PJLdO7cWa3906dPsXnzZpw7dw4pKSmoXr066tWrhw4dOmDu3LmQSqWqtvn5+di5cycOHTqExMRESCQStGzZEj4+Pvj444/19dYQ0RuQKBQKhdhBEBEJoSzGunXrhoiICHTu3BnOzs7466+/sHbtWsydOxeHDx+Gg4MDOnfuDDMzM4SHhyMhIQHjx4/H119/repr1qxZCA0NhbOzs6rtX3/9hStXruDLL7+Et7c3gP8rxnr27Inw8HD069cPDRo0QGRkJG7cuIEWLVpg7969qF69uqrv48ePY86cOTA1NUX//v1ha2uLixcv4s6dO/jggw+wbds2mJmZqdo7OzujRYsWePnyJWrWrImOHTsiPT0doaGhMDIyQnBwMJycnAAAeXl58PDwgEwmQ7du3eDs7Ay5XI7Hjx/j0qVLOHLkCBo1agSguBDz8fFBREQEWrVqhQ4dOkAul+Ps2bP466+/sGTJEowePboi3joiKo2CiKiSuHz5ssLJyUnh5OSkCAoKUtu3d+9ehZOTk2LhwoUKuVyu2v7q1SvFpEmTFE5OTopbt24pFAqF4vnz5wpnZ2eFt7e3oqCgQK2fgoICRXZ2turxgQMHVGNevHhRre38+fMVTk5OisDAQNW2Fy9eKDp27Kho3bq14u7du6rtRUVFijlz5iicnJwUP/30k1o/yv6XLVumKCwsVG3ft2+fwsnJSbFkyRLVtlOnTimcnJwUK1eu1MjPixcvFK9evVI9Xrt2rcLJyUkREBCgKCoqUmvn7e2teP/99xUpKSka/RBRxeKcMSKqdFq1aqVx2XHHjh0wNzfH//zP/8DE5P9mYJiZmWH27NkAgCNHjgAAJBIJFAoFzMzMYGxsrNaPsbExLC0tNcbs1q0b3Nzc1LbNmjULpqamOHjwoGrbyZMn8fz5cwwZMgQtWrRQbZdIJJg7dy5MTEwQEhKi0b+5uTnmzp0LI6P/+1r28vKCiYkJbt++rdG+5Jk4JalUqjrjVlRUhN27d6NJkyaYNm0aJBKJWrtp06ZBLpfjxIkTGv0QUcXinDEiqnTatGmj9jgvLw+xsbGoW7cuNm/erNG+oKAAAJCQkACguBjp3r07zp8/Dy8vL/Tt2xcdO3aEi4uL2uXDkjp06KCxrV69erC3t0dCQgJycnIglUpx9+5dAICrq6tG+wYNGsDe3h6JiYmq9kpNmjRBzZo11dqbmJjA1tYWz58/V23r1KkT6tSpg02bNuHu3bvo0aMHOnToAGdnZ7WCKzExEdnZ2ahbty4CAwM1YsnIyFDLCRGJh8UYEVU6tWvXVnv8/PlzKBQKpKamai08lHJzc1X/9vf3x6ZNm3D48GGsW7cOAFCzZk0MGTIEc+bM0Zhkb2tr+9pYEhIS8Pfff0MqlSInJ0drjEp16tRBYmKiqr2ShYWF1vYmJiYoKipSa7dnzx4EBATgzz//xNmzZwEA9evXx8SJE/HZZ58BALKysgAADx48wIMHD16bk7y8vNfuI6KKwWKMiCqdkmeAAKjOKL3//vsIDg4W1Ie5uTlmz56N2bNn4/Hjx7hy5Qr27NmDbdu24dWrV1i+fLla+/T0dK39PHv2TC0GZYGl3F5We100atQI33//PQoLCxEbG4sLFy5g+/btWL58OaysrODh4aGKo2/fvvD399d5LCJ6+zhnjIgqPalUiubNmyMhIUHtkp5Q9vb2GDp0qGre2enTpzXaXLt2TWNbamoqHj9+jMaNG6uKn5YtWwIAIiIiXtve3t5e7ayYroyNjdGyZUtMmDABa9euBQBV7M2bN4dUKsXt27chl8vfeCwientYjBHRO+Hzzz9HXl4eFi9erHY5Uunx48dITk4GUDxfKjo6WqNNdnY25HI5qlWrprEvPDwcly5dUtu2fv16yOVyeHp6qrb17t0bFhYWCA4OVrs8qFAosGbNGsjlcnh5een6MhEbGwuZTKaxXXnGTRm7iYkJPv30U8hkMnz//fdaC7LY2NjXnvEjoorDy5RE9E4YOXIkbt68iZCQEFy/fh1du3ZF3bp1kZ6ejoSEBNy8eRNr1qxBo0aNkJqaimHDhuG9995Dq1atUK9ePWRlZeHUqVOQy+Xw9fXV6L9nz56YMGGC1nXGfHx8VO2kUim+/fZbfPnllxg+fDj69+8PGxsbXLp0Cbdv34aLi4vW/oW6ePEivv/+e7Rv3x7NmjVDrVq18PjxY5w+fRrVq1dXWzdsxowZiImJwfbt23H27Fl06tQJNjY2SE1NRWxsLO7du4e9e/e+dj4cEVUMFmNE9E6QSCRYtWoVPvzwQwQFBeHMmTPIzc2FjY0NmjRpgq+//lq1NIWdnR2mT5+Oy5cv4+LFi8jKyoK1tTVatWqFcePGwd3dXaP/vn37YujQofj5558RFhYGqVSKESNGYM6cORrLTPTv31/1i8cTJ06oVuCfOnUqJkyYoPXMm1Ddu3eHTCbD1atXERYWhtzcXNSrV0+1un/z5s1Vbc3MzPDLL79g//79OHjwII4fP478/HzUrl0bzZs3x8iRI1WLyRKReLgCPxFRKUreDkm5Kj8RkT5xzhgRERGRiFiMEREREYmIxRgRERGRiDhnjIiIiEhEPDNGREREJCIWY0REREQiYjFGREREJCIWY0REREQiYjFGREREJCIWY0REREQiYjFGREREJCIWY0REREQi+n+g5Dq2JYEfXwAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 600x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "run_basic_trolley_counterfactual(action=0, intent=1, contact=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "404a8eb8",
   "metadata": {},
   "source": [
    "## What about competing causes?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "92255849-ea0a-4054-bf79-8b35488143a9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/svg+xml": [
       "<?xml version=\"1.0\" encoding=\"UTF-8\" standalone=\"no\"?>\n",
       "<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n",
       " \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\n",
       "<!-- Generated by graphviz version 12.0.0 (20240803.0821)\n",
       " -->\n",
       "<!-- Pages: 1 -->\n",
       "<svg width=\"610pt\" height=\"231pt\"\n",
       " viewBox=\"0.00 0.00 610.33 230.72\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n",
       "<g id=\"graph0\" class=\"graph\" transform=\"scale(1 1) rotate(0) translate(4 226.72)\">\n",
       "<polygon fill=\"white\" stroke=\"none\" points=\"-4,4 -4,-226.72 606.33,-226.72 606.33,4 -4,4\"/>\n",
       "<!-- X -->\n",
       "<g id=\"node1\" class=\"node\">\n",
       "<title>X</title>\n",
       "<ellipse fill=\"none\" stroke=\"red\" cx=\"291.09\" cy=\"-203.28\" rx=\"145.49\" ry=\"19.45\"/>\n",
       "<text text-anchor=\"middle\" x=\"291.09\" y=\"-199.73\" font-family=\"Times,serif\" font-size=\"14.00\">action, intenition, contact, X</text>\n",
       "</g>\n",
       "<!-- R -->\n",
       "<g id=\"node2\" class=\"node\">\n",
       "<title>R</title>\n",
       "<ellipse fill=\"none\" stroke=\"red\" cx=\"537.45\" cy=\"-99.28\" rx=\"64.88\" ry=\"19.45\"/>\n",
       "<text text-anchor=\"middle\" x=\"537.45\" y=\"-95.73\" font-family=\"Times,serif\" font-size=\"14.00\">response, R</text>\n",
       "</g>\n",
       "<!-- X&#45;&gt;R -->\n",
       "<g id=\"edge1\" class=\"edge\">\n",
       "<title>X&#45;&gt;R</title>\n",
       "<path fill=\"none\" stroke=\"red\" d=\"M393,-189.05C407.95,-185.23 422.92,-180.4 436.58,-174.28 463.4,-162.27 489.82,-141.95 508.74,-125.49\"/>\n",
       "<polygon fill=\"red\" stroke=\"red\" points=\"510.87,-128.28 516.01,-119.02 506.21,-123.06 510.87,-128.28\"/>\n",
       "</g>\n",
       "<!-- S -->\n",
       "<g id=\"node3\" class=\"node\">\n",
       "<title>S</title>\n",
       "<ellipse fill=\"none\" stroke=\"black\" cx=\"291.09\" cy=\"-146.28\" rx=\"45.25\" ry=\"19.45\"/>\n",
       "<text text-anchor=\"middle\" x=\"291.09\" y=\"-142.73\" font-family=\"Times,serif\" font-size=\"14.00\">story, S</text>\n",
       "</g>\n",
       "<!-- S&#45;&gt;R -->\n",
       "<g id=\"edge2\" class=\"edge\">\n",
       "<title>S&#45;&gt;R</title>\n",
       "<path fill=\"none\" stroke=\"blue\" d=\"M332.99,-138.41C370.61,-131.18 427.05,-120.32 470.95,-111.88\"/>\n",
       "<polygon fill=\"blue\" stroke=\"blue\" points=\"471.46,-115.34 480.62,-110.02 470.14,-108.47 471.46,-115.34\"/>\n",
       "</g>\n",
       "<!-- E -->\n",
       "<g id=\"node4\" class=\"node\">\n",
       "<title>E</title>\n",
       "<ellipse fill=\"none\" stroke=\"black\" cx=\"291.09\" cy=\"-52.28\" rx=\"70.18\" ry=\"19.45\"/>\n",
       "<text text-anchor=\"middle\" x=\"291.09\" y=\"-48.73\" font-family=\"Times,serif\" font-size=\"14.00\">education, E</text>\n",
       "</g>\n",
       "<!-- E&#45;&gt;R -->\n",
       "<g id=\"edge3\" class=\"edge\">\n",
       "<title>E&#45;&gt;R</title>\n",
       "<path fill=\"none\" stroke=\"blue\" d=\"M349.69,-63.36C386.16,-70.37 433.41,-79.46 471.26,-86.74\"/>\n",
       "<polygon fill=\"blue\" stroke=\"blue\" points=\"470.2,-90.1 480.68,-88.55 471.53,-83.23 470.2,-90.1\"/>\n",
       "</g>\n",
       "<!-- Y -->\n",
       "<g id=\"node5\" class=\"node\">\n",
       "<title>Y</title>\n",
       "<ellipse fill=\"none\" stroke=\"black\" cx=\"54.8\" cy=\"-89.28\" rx=\"39.42\" ry=\"19.45\"/>\n",
       "<text text-anchor=\"middle\" x=\"54.8\" y=\"-85.73\" font-family=\"Times,serif\" font-size=\"14.00\">age, Y</text>\n",
       "</g>\n",
       "<!-- Y&#45;&gt;R -->\n",
       "<g id=\"edge4\" class=\"edge\">\n",
       "<title>Y&#45;&gt;R</title>\n",
       "<path fill=\"none\" stroke=\"blue\" d=\"M94.32,-90.08C173.77,-91.73 357.05,-95.55 460.97,-97.71\"/>\n",
       "<polygon fill=\"blue\" stroke=\"blue\" points=\"460.73,-101.2 470.8,-97.91 460.87,-94.21 460.73,-101.2\"/>\n",
       "</g>\n",
       "<!-- Y&#45;&gt;E -->\n",
       "<g id=\"edge6\" class=\"edge\">\n",
       "<title>Y&#45;&gt;E</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M92.92,-83.42C126.49,-78.12 176.85,-70.16 218.27,-63.62\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"218.59,-67.11 227.92,-62.1 217.5,-60.2 218.59,-67.11\"/>\n",
       "</g>\n",
       "<!-- G -->\n",
       "<g id=\"node6\" class=\"node\">\n",
       "<title>G</title>\n",
       "<ellipse fill=\"none\" stroke=\"black\" cx=\"54.8\" cy=\"-23.28\" rx=\"54.8\" ry=\"19.45\"/>\n",
       "<text text-anchor=\"middle\" x=\"54.8\" y=\"-19.73\" font-family=\"Times,serif\" font-size=\"14.00\">gender, G</text>\n",
       "</g>\n",
       "<!-- G&#45;&gt;R -->\n",
       "<g id=\"edge5\" class=\"edge\">\n",
       "<title>G&#45;&gt;R</title>\n",
       "<path fill=\"none\" stroke=\"blue\" d=\"M103.65,-14C177.37,-1.76 322.18,14.06 436.58,-24.28 464.98,-33.8 491.8,-54.88 510.49,-72.27\"/>\n",
       "<polygon fill=\"blue\" stroke=\"blue\" points=\"508.01,-74.74 517.64,-79.15 512.86,-69.69 508.01,-74.74\"/>\n",
       "</g>\n",
       "<!-- G&#45;&gt;E -->\n",
       "<g id=\"edge7\" class=\"edge\">\n",
       "<title>G&#45;&gt;E</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M106.93,-29.61C138.64,-33.53 179.93,-38.64 215.28,-43.02\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"214.67,-46.47 225.03,-44.23 215.53,-39.52 214.67,-46.47\"/>\n",
       "</g>\n",
       "</g>\n",
       "</svg>\n"
      ],
      "text/plain": [
       "<graphviz.graphs.Digraph at 0x7f33ebd3f1a0>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "utils.draw_causal_graph(\n",
    "    edge_list=[(\"X\", \"R\"), (\"S\", \"R\"), (\"E\", \"R\"), (\"Y\", \"R\"), (\"G\", \"R\"), (\"Y\", \"E\"), (\"G\", \"E\")],\n",
    "    node_props={\n",
    "        \"X\": {\"color\": \"red\", \"label\": \"action, intenition, contact, X\"},\n",
    "        \"R\": {\"color\": \"red\", \"label\": \"response, R\"},\n",
    "        \"S\": {\"label\": \"story, S\"},\n",
    "        \"E\": {\"label\": \"education, E\"},\n",
    "        \"Y\": {\"label\": \"age, Y\"},\n",
    "        \"G\": {\"label\": \"gender, G\"},\n",
    "    },\n",
    "    edge_props={\n",
    "        (\"X\", \"R\"): {\"color\": \"red\"},\n",
    "        (\"Y\", \"R\"): {\"color\": \"blue\"},\n",
    "        (\"E\", \"R\"): {\"color\": \"blue\"},\n",
    "        (\"S\", \"R\"): {\"color\": \"blue\"},\n",
    "        (\"G\", \"R\"): {\"color\": \"blue\"},\n",
    "    },\n",
    "    graph_direction=\"LR\",\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5feb0f49",
   "metadata": {},
   "source": [
    "### Total effect of gender"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "fac5b965",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/svg+xml": [
       "<?xml version=\"1.0\" encoding=\"UTF-8\" standalone=\"no\"?>\n",
       "<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n",
       " \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\n",
       "<!-- Generated by graphviz version 12.0.0 (20240803.0821)\n",
       " -->\n",
       "<!-- Pages: 1 -->\n",
       "<svg width=\"610pt\" height=\"231pt\"\n",
       " viewBox=\"0.00 0.00 610.33 230.72\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n",
       "<g id=\"graph0\" class=\"graph\" transform=\"scale(1 1) rotate(0) translate(4 226.72)\">\n",
       "<polygon fill=\"white\" stroke=\"none\" points=\"-4,4 -4,-226.72 606.33,-226.72 606.33,4 -4,4\"/>\n",
       "<!-- X -->\n",
       "<g id=\"node1\" class=\"node\">\n",
       "<title>X</title>\n",
       "<ellipse fill=\"none\" stroke=\"red\" cx=\"291.09\" cy=\"-203.28\" rx=\"145.49\" ry=\"19.45\"/>\n",
       "<text text-anchor=\"middle\" x=\"291.09\" y=\"-199.73\" font-family=\"Times,serif\" font-size=\"14.00\">action, intenition, contact, X</text>\n",
       "</g>\n",
       "<!-- R -->\n",
       "<g id=\"node2\" class=\"node\">\n",
       "<title>R</title>\n",
       "<ellipse fill=\"none\" stroke=\"red\" cx=\"537.45\" cy=\"-99.28\" rx=\"64.88\" ry=\"19.45\"/>\n",
       "<text text-anchor=\"middle\" x=\"537.45\" y=\"-95.73\" font-family=\"Times,serif\" font-size=\"14.00\">response, R</text>\n",
       "</g>\n",
       "<!-- X&#45;&gt;R -->\n",
       "<g id=\"edge1\" class=\"edge\">\n",
       "<title>X&#45;&gt;R</title>\n",
       "<path fill=\"none\" stroke=\"red\" d=\"M393,-189.05C407.95,-185.23 422.92,-180.4 436.58,-174.28 463.4,-162.27 489.82,-141.95 508.74,-125.49\"/>\n",
       "<polygon fill=\"red\" stroke=\"red\" points=\"510.87,-128.28 516.01,-119.02 506.21,-123.06 510.87,-128.28\"/>\n",
       "</g>\n",
       "<!-- S -->\n",
       "<g id=\"node3\" class=\"node\">\n",
       "<title>S</title>\n",
       "<ellipse fill=\"none\" stroke=\"black\" cx=\"291.09\" cy=\"-146.28\" rx=\"45.25\" ry=\"19.45\"/>\n",
       "<text text-anchor=\"middle\" x=\"291.09\" y=\"-142.73\" font-family=\"Times,serif\" font-size=\"14.00\">story, S</text>\n",
       "</g>\n",
       "<!-- S&#45;&gt;R -->\n",
       "<g id=\"edge2\" class=\"edge\">\n",
       "<title>S&#45;&gt;R</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M332.99,-138.41C370.61,-131.18 427.05,-120.32 470.95,-111.88\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"471.46,-115.34 480.62,-110.02 470.14,-108.47 471.46,-115.34\"/>\n",
       "</g>\n",
       "<!-- E -->\n",
       "<g id=\"node4\" class=\"node\">\n",
       "<title>E</title>\n",
       "<ellipse fill=\"none\" stroke=\"black\" cx=\"291.09\" cy=\"-52.28\" rx=\"70.18\" ry=\"19.45\"/>\n",
       "<text text-anchor=\"middle\" x=\"291.09\" y=\"-48.73\" font-family=\"Times,serif\" font-size=\"14.00\">education, E</text>\n",
       "</g>\n",
       "<!-- E&#45;&gt;R -->\n",
       "<g id=\"edge3\" class=\"edge\">\n",
       "<title>E&#45;&gt;R</title>\n",
       "<path fill=\"none\" stroke=\"blue\" d=\"M349.69,-63.36C386.16,-70.37 433.41,-79.46 471.26,-86.74\"/>\n",
       "<polygon fill=\"blue\" stroke=\"blue\" points=\"470.2,-90.1 480.68,-88.55 471.53,-83.23 470.2,-90.1\"/>\n",
       "</g>\n",
       "<!-- Y -->\n",
       "<g id=\"node5\" class=\"node\">\n",
       "<title>Y</title>\n",
       "<ellipse fill=\"none\" stroke=\"black\" cx=\"54.8\" cy=\"-89.28\" rx=\"39.42\" ry=\"19.45\"/>\n",
       "<text text-anchor=\"middle\" x=\"54.8\" y=\"-85.73\" font-family=\"Times,serif\" font-size=\"14.00\">age, Y</text>\n",
       "</g>\n",
       "<!-- Y&#45;&gt;R -->\n",
       "<g id=\"edge4\" class=\"edge\">\n",
       "<title>Y&#45;&gt;R</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M94.32,-90.08C173.77,-91.73 357.05,-95.55 460.97,-97.71\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"460.73,-101.2 470.8,-97.91 460.87,-94.21 460.73,-101.2\"/>\n",
       "</g>\n",
       "<!-- Y&#45;&gt;E -->\n",
       "<g id=\"edge6\" class=\"edge\">\n",
       "<title>Y&#45;&gt;E</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M92.92,-83.42C126.49,-78.12 176.85,-70.16 218.27,-63.62\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"218.59,-67.11 227.92,-62.1 217.5,-60.2 218.59,-67.11\"/>\n",
       "</g>\n",
       "<!-- G -->\n",
       "<g id=\"node6\" class=\"node\">\n",
       "<title>G</title>\n",
       "<ellipse fill=\"none\" stroke=\"blue\" cx=\"54.8\" cy=\"-23.28\" rx=\"54.8\" ry=\"19.45\"/>\n",
       "<text text-anchor=\"middle\" x=\"54.8\" y=\"-19.73\" font-family=\"Times,serif\" font-size=\"14.00\">gender, G</text>\n",
       "</g>\n",
       "<!-- G&#45;&gt;R -->\n",
       "<g id=\"edge5\" class=\"edge\">\n",
       "<title>G&#45;&gt;R</title>\n",
       "<path fill=\"none\" stroke=\"blue\" d=\"M103.65,-14C177.37,-1.76 322.18,14.06 436.58,-24.28 464.98,-33.8 491.8,-54.88 510.49,-72.27\"/>\n",
       "<polygon fill=\"blue\" stroke=\"blue\" points=\"508.01,-74.74 517.64,-79.15 512.86,-69.69 508.01,-74.74\"/>\n",
       "</g>\n",
       "<!-- G&#45;&gt;E -->\n",
       "<g id=\"edge7\" class=\"edge\">\n",
       "<title>G&#45;&gt;E</title>\n",
       "<path fill=\"none\" stroke=\"blue\" d=\"M106.93,-29.61C138.64,-33.53 179.93,-38.64 215.28,-43.02\"/>\n",
       "<polygon fill=\"blue\" stroke=\"blue\" points=\"214.67,-46.47 225.03,-44.23 215.53,-39.52 214.67,-46.47\"/>\n",
       "</g>\n",
       "</g>\n",
       "</svg>\n"
      ],
      "text/plain": [
       "<graphviz.graphs.Digraph at 0x7f33f04e9df0>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "utils.draw_causal_graph(\n",
    "    edge_list=[(\"X\", \"R\"), (\"S\", \"R\"), (\"E\", \"R\"), (\"Y\", \"R\"), (\"G\", \"R\"), (\"Y\", \"E\"), (\"G\", \"E\")],\n",
    "    node_props={\n",
    "        \"X\": {\"label\": \"action, intenition, contact, X\", \"color\": \"red\"},\n",
    "        \"R\": {\"color\": \"red\", \"label\": \"response, R\"},\n",
    "        \"S\": {\"label\": \"story, S\"},\n",
    "        \"E\": {\"label\": \"education, E\"},\n",
    "        \"Y\": {\"label\": \"age, Y\"},\n",
    "        \"G\": {\"label\": \"gender, G\", \"color\": \"blue\"},\n",
    "    },\n",
    "    edge_props={\n",
    "        (\"G\", \"R\"): {\"color\": \"blue\"},\n",
    "        (\"G\", \"E\"): {\"color\": \"blue\"},\n",
    "        (\"E\", \"R\"): {\"color\": \"blue\"},\n",
    "        (\"X\", \"R\"): {\"color\": \"red\"},\n",
    "    },\n",
    "    graph_direction=\"LR\",\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f32017c6-94a6-43a7-b13c-6717ffcbe45e",
   "metadata": {},
   "source": [
    "$$\n",
    "\\begin{align*}\n",
    "R_i &\\sim OrderedLogit(\\phi_i, \\alpha) \\\\\n",
    "\\phi_i &= \\beta_{A, G[i]} A_i + \\beta_{C, G[i]} C_i + \\beta_{I, G[i]} I_i \\\\\n",
    "\\alpha_j &\\sim \\mathcal N(0,1) \\\\\n",
    "\\beta_* &\\sim \\mathcal N(0, .5)\n",
    "\\end{align*}\n",
    "$$"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3133546e-2bde-47c4-8adf-b65085e65e7d",
   "metadata": {},
   "source": [
    "### Fit the gender-stratified model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "d7ba616b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/svg+xml": [
       "<?xml version=\"1.0\" encoding=\"UTF-8\" standalone=\"no\"?>\n",
       "<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n",
       " \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\n",
       "<!-- Generated by graphviz version 12.0.0 (20240803.0821)\n",
       " -->\n",
       "<!-- Pages: 1 -->\n",
       "<svg width=\"1072pt\" height=\"280pt\"\n",
       " viewBox=\"0.00 0.00 1072.00 279.55\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n",
       "<g id=\"graph0\" class=\"graph\" transform=\"scale(1 1) rotate(0) translate(4 275.55)\">\n",
       "<polygon fill=\"white\" stroke=\"none\" points=\"-4,4 -4,-275.55 1068,-275.55 1068,4 -4,4\"/>\n",
       "<g id=\"clust1\" class=\"cluster\">\n",
       "<title>cluster9930</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M20,-126C20,-126 457,-126 457,-126 463,-126 469,-132 469,-138 469,-138 469,-251.55 469,-251.55 469,-257.55 463,-263.55 457,-263.55 457,-263.55 20,-263.55 20,-263.55 14,-263.55 8,-257.55 8,-251.55 8,-251.55 8,-138 8,-138 8,-132 14,-126 20,-126\"/>\n",
       "<text text-anchor=\"middle\" x=\"446\" y=\"-136.2\" font-family=\"Times,serif\" font-size=\"14.00\">9930</text>\n",
       "</g>\n",
       "<g id=\"clust2\" class=\"cluster\">\n",
       "<title>clusterGENDER (2)</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M489,-126C489,-126 924,-126 924,-126 930,-126 936,-132 936,-138 936,-138 936,-251.55 936,-251.55 936,-257.55 930,-263.55 924,-263.55 924,-263.55 489,-263.55 489,-263.55 483,-263.55 477,-257.55 477,-251.55 477,-251.55 477,-138 477,-138 477,-132 483,-126 489,-126\"/>\n",
       "<text text-anchor=\"middle\" x=\"889\" y=\"-136.2\" font-family=\"Times,serif\" font-size=\"14.00\">GENDER (2)</text>\n",
       "</g>\n",
       "<g id=\"clust3\" class=\"cluster\">\n",
       "<title>clusterCUTPOINTS (6)</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M956,-126C956,-126 1044,-126 1044,-126 1050,-126 1056,-132 1056,-138 1056,-138 1056,-251.55 1056,-251.55 1056,-257.55 1050,-263.55 1044,-263.55 1044,-263.55 956,-263.55 956,-263.55 950,-263.55 944,-257.55 944,-251.55 944,-251.55 944,-138 944,-138 944,-132 950,-126 956,-126\"/>\n",
       "<text text-anchor=\"middle\" x=\"999.62\" y=\"-136.2\" font-family=\"Times,serif\" font-size=\"14.00\">CUTPOINTS (6)</text>\n",
       "</g>\n",
       "<g id=\"clust4\" class=\"cluster\">\n",
       "<title>cluster9930 x 7</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M377,-8C377,-8 489,-8 489,-8 495,-8 501,-14 501,-20 501,-20 501,-106 501,-106 501,-112 495,-118 489,-118 489,-118 377,-118 377,-118 371,-118 365,-112 365,-106 365,-106 365,-20 365,-20 365,-14 371,-8 377,-8\"/>\n",
       "<text text-anchor=\"middle\" x=\"466.38\" y=\"-18.2\" font-family=\"Times,serif\" font-size=\"14.00\">9930 x 7</text>\n",
       "</g>\n",
       "<!-- response -->\n",
       "<g id=\"node1\" class=\"node\">\n",
       "<title>response</title>\n",
       "<ellipse fill=\"lightgrey\" stroke=\"black\" cx=\"81\" cy=\"-208.52\" rx=\"64.88\" ry=\"47.02\"/>\n",
       "<text text-anchor=\"middle\" x=\"81\" y=\"-224.47\" font-family=\"Times,serif\" font-size=\"14.00\">response</text>\n",
       "<text text-anchor=\"middle\" x=\"81\" y=\"-204.97\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
       "<text text-anchor=\"middle\" x=\"81\" y=\"-185.47\" font-family=\"Times,serif\" font-size=\"14.00\">Categorical</text>\n",
       "</g>\n",
       "<!-- contact -->\n",
       "<g id=\"node2\" class=\"node\">\n",
       "<title>contact</title>\n",
       "<path fill=\"lightgrey\" stroke=\"black\" d=\"M217.75,-241.77C217.75,-241.77 176.25,-241.77 176.25,-241.77 170.25,-241.77 164.25,-235.77 164.25,-229.77 164.25,-229.77 164.25,-187.27 164.25,-187.27 164.25,-181.27 170.25,-175.27 176.25,-175.27 176.25,-175.27 217.75,-175.27 217.75,-175.27 223.75,-175.27 229.75,-181.27 229.75,-187.27 229.75,-187.27 229.75,-229.77 229.75,-229.77 229.75,-235.77 223.75,-241.77 217.75,-241.77\"/>\n",
       "<text text-anchor=\"middle\" x=\"197\" y=\"-224.47\" font-family=\"Times,serif\" font-size=\"14.00\">contact</text>\n",
       "<text text-anchor=\"middle\" x=\"197\" y=\"-204.97\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
       "<text text-anchor=\"middle\" x=\"197\" y=\"-185.47\" font-family=\"Times,serif\" font-size=\"14.00\">Data</text>\n",
       "</g>\n",
       "<!-- response_probs -->\n",
       "<g id=\"node10\" class=\"node\">\n",
       "<title>response_probs</title>\n",
       "<polygon fill=\"none\" stroke=\"black\" points=\"492.75,-110 373.25,-110 373.25,-43.5 492.75,-43.5 492.75,-110\"/>\n",
       "<text text-anchor=\"middle\" x=\"433\" y=\"-92.7\" font-family=\"Times,serif\" font-size=\"14.00\">response_probs</text>\n",
       "<text text-anchor=\"middle\" x=\"433\" y=\"-73.2\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
       "<text text-anchor=\"middle\" x=\"433\" y=\"-53.7\" font-family=\"Times,serif\" font-size=\"14.00\">Deterministic</text>\n",
       "</g>\n",
       "<!-- contact&#45;&gt;response_probs -->\n",
       "<g id=\"edge2\" class=\"edge\">\n",
       "<title>contact&#45;&gt;response_probs</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M206.48,-175.03C213.01,-158.08 223.39,-138.35 239,-126 273.75,-98.5 322.11,-86.43 361.84,-81.24\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"362.04,-84.74 371.57,-80.1 361.23,-77.79 362.04,-84.74\"/>\n",
       "</g>\n",
       "<!-- action -->\n",
       "<g id=\"node3\" class=\"node\">\n",
       "<title>action</title>\n",
       "<path fill=\"lightgrey\" stroke=\"black\" d=\"M294,-241.77C294,-241.77 260,-241.77 260,-241.77 254,-241.77 248,-235.77 248,-229.77 248,-229.77 248,-187.27 248,-187.27 248,-181.27 254,-175.27 260,-175.27 260,-175.27 294,-175.27 294,-175.27 300,-175.27 306,-181.27 306,-187.27 306,-187.27 306,-229.77 306,-229.77 306,-235.77 300,-241.77 294,-241.77\"/>\n",
       "<text text-anchor=\"middle\" x=\"277\" y=\"-224.47\" font-family=\"Times,serif\" font-size=\"14.00\">action</text>\n",
       "<text text-anchor=\"middle\" x=\"277\" y=\"-204.97\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
       "<text text-anchor=\"middle\" x=\"277\" y=\"-185.47\" font-family=\"Times,serif\" font-size=\"14.00\">Data</text>\n",
       "</g>\n",
       "<!-- action&#45;&gt;response_probs -->\n",
       "<g id=\"edge5\" class=\"edge\">\n",
       "<title>action&#45;&gt;response_probs</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M285.85,-175.1C291.78,-158.59 301.1,-139.25 315,-126 328.27,-113.35 345.3,-103.84 362.27,-96.76\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"363.34,-100.1 371.39,-93.2 360.8,-93.57 363.34,-100.1\"/>\n",
       "</g>\n",
       "<!-- gender -->\n",
       "<g id=\"node4\" class=\"node\">\n",
       "<title>gender</title>\n",
       "<path fill=\"lightgrey\" stroke=\"black\" d=\"M374.25,-241.77C374.25,-241.77 335.75,-241.77 335.75,-241.77 329.75,-241.77 323.75,-235.77 323.75,-229.77 323.75,-229.77 323.75,-187.27 323.75,-187.27 323.75,-181.27 329.75,-175.27 335.75,-175.27 335.75,-175.27 374.25,-175.27 374.25,-175.27 380.25,-175.27 386.25,-181.27 386.25,-187.27 386.25,-187.27 386.25,-229.77 386.25,-229.77 386.25,-235.77 380.25,-241.77 374.25,-241.77\"/>\n",
       "<text text-anchor=\"middle\" x=\"355\" y=\"-224.47\" font-family=\"Times,serif\" font-size=\"14.00\">gender</text>\n",
       "<text text-anchor=\"middle\" x=\"355\" y=\"-204.97\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
       "<text text-anchor=\"middle\" x=\"355\" y=\"-185.47\" font-family=\"Times,serif\" font-size=\"14.00\">Data</text>\n",
       "</g>\n",
       "<!-- gender&#45;&gt;response_probs -->\n",
       "<g id=\"edge6\" class=\"edge\">\n",
       "<title>gender&#45;&gt;response_probs</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M369.61,-175.02C376.9,-159.77 386.24,-141.57 396,-126 397.32,-123.89 398.71,-121.76 400.15,-119.64\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"402.9,-121.81 405.8,-111.62 397.18,-117.78 402.9,-121.81\"/>\n",
       "</g>\n",
       "<!-- intent -->\n",
       "<g id=\"node5\" class=\"node\">\n",
       "<title>intent</title>\n",
       "<path fill=\"lightgrey\" stroke=\"black\" d=\"M449.25,-241.77C449.25,-241.77 416.75,-241.77 416.75,-241.77 410.75,-241.77 404.75,-235.77 404.75,-229.77 404.75,-229.77 404.75,-187.27 404.75,-187.27 404.75,-181.27 410.75,-175.27 416.75,-175.27 416.75,-175.27 449.25,-175.27 449.25,-175.27 455.25,-175.27 461.25,-181.27 461.25,-187.27 461.25,-187.27 461.25,-229.77 461.25,-229.77 461.25,-235.77 455.25,-241.77 449.25,-241.77\"/>\n",
       "<text text-anchor=\"middle\" x=\"433\" y=\"-224.47\" font-family=\"Times,serif\" font-size=\"14.00\">intent</text>\n",
       "<text text-anchor=\"middle\" x=\"433\" y=\"-204.97\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
       "<text text-anchor=\"middle\" x=\"433\" y=\"-185.47\" font-family=\"Times,serif\" font-size=\"14.00\">Data</text>\n",
       "</g>\n",
       "<!-- intent&#45;&gt;response_probs -->\n",
       "<g id=\"edge7\" class=\"edge\">\n",
       "<title>intent&#45;&gt;response_probs</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M433,-174.78C433,-158.64 433,-138.95 433,-121.54\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"436.5,-122 433,-112 429.5,-122 436.5,-122\"/>\n",
       "</g>\n",
       "<!-- beta_contact -->\n",
       "<g id=\"node6\" class=\"node\">\n",
       "<title>beta_contact</title>\n",
       "<ellipse fill=\"none\" stroke=\"black\" cx=\"557\" cy=\"-208.52\" rx=\"71.77\" ry=\"47.02\"/>\n",
       "<text text-anchor=\"middle\" x=\"557\" y=\"-224.47\" font-family=\"Times,serif\" font-size=\"14.00\">beta_contact</text>\n",
       "<text text-anchor=\"middle\" x=\"557\" y=\"-204.97\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
       "<text text-anchor=\"middle\" x=\"557\" y=\"-185.47\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
       "</g>\n",
       "<!-- beta_contact&#45;&gt;response_probs -->\n",
       "<g id=\"edge1\" class=\"edge\">\n",
       "<title>beta_contact&#45;&gt;response_probs</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M519.46,-168.23C504.41,-152.48 487.06,-134.33 471.88,-118.44\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"474.64,-116.26 465.2,-111.45 469.58,-121.1 474.64,-116.26\"/>\n",
       "</g>\n",
       "<!-- beta_action -->\n",
       "<g id=\"node7\" class=\"node\">\n",
       "<title>beta_action</title>\n",
       "<ellipse fill=\"none\" stroke=\"black\" cx=\"713\" cy=\"-208.52\" rx=\"66.47\" ry=\"47.02\"/>\n",
       "<text text-anchor=\"middle\" x=\"713\" y=\"-224.47\" font-family=\"Times,serif\" font-size=\"14.00\">beta_action</text>\n",
       "<text text-anchor=\"middle\" x=\"713\" y=\"-204.97\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
       "<text text-anchor=\"middle\" x=\"713\" y=\"-185.47\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
       "</g>\n",
       "<!-- beta_action&#45;&gt;response_probs -->\n",
       "<g id=\"edge8\" class=\"edge\">\n",
       "<title>beta_action&#45;&gt;response_probs</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M683.34,-166.11C670.88,-151.44 655.27,-136.1 638,-126 597.32,-102.21 545.64,-90.15 504.35,-84.03\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"504.88,-80.57 494.5,-82.67 503.92,-87.51 504.88,-80.57\"/>\n",
       "</g>\n",
       "<!-- beta_intent -->\n",
       "<g id=\"node8\" class=\"node\">\n",
       "<title>beta_intent</title>\n",
       "<ellipse fill=\"none\" stroke=\"black\" cx=\"863\" cy=\"-208.52\" rx=\"65.41\" ry=\"47.02\"/>\n",
       "<text text-anchor=\"middle\" x=\"863\" y=\"-224.47\" font-family=\"Times,serif\" font-size=\"14.00\">beta_intent</text>\n",
       "<text text-anchor=\"middle\" x=\"863\" y=\"-204.97\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
       "<text text-anchor=\"middle\" x=\"863\" y=\"-185.47\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
       "</g>\n",
       "<!-- beta_intent&#45;&gt;response_probs -->\n",
       "<g id=\"edge4\" class=\"edge\">\n",
       "<title>beta_intent&#45;&gt;response_probs</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M834.54,-166.07C822.04,-151.03 806.07,-135.43 788,-126 740.06,-100.98 593.47,-87.69 504.36,-81.74\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"504.7,-78.25 494.49,-81.09 504.24,-85.24 504.7,-78.25\"/>\n",
       "</g>\n",
       "<!-- alpha -->\n",
       "<g id=\"node9\" class=\"node\">\n",
       "<title>alpha</title>\n",
       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1000\" cy=\"-208.52\" rx=\"47.91\" ry=\"47.02\"/>\n",
       "<text text-anchor=\"middle\" x=\"1000\" y=\"-224.47\" font-family=\"Times,serif\" font-size=\"14.00\">alpha</text>\n",
       "<text text-anchor=\"middle\" x=\"1000\" y=\"-204.97\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
       "<text text-anchor=\"middle\" x=\"1000\" y=\"-185.47\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
       "</g>\n",
       "<!-- alpha&#45;&gt;response_probs -->\n",
       "<g id=\"edge3\" class=\"edge\">\n",
       "<title>alpha&#45;&gt;response_probs</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M979.03,-165.82C969.28,-150.74 956.21,-135.18 940,-126 867.34,-84.82 625.84,-78.14 504.25,-77.47\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"504.39,-73.97 494.38,-77.43 504.36,-80.97 504.39,-73.97\"/>\n",
       "</g>\n",
       "<!-- response_probs&#45;&gt;response -->\n",
       "<g id=\"edge9\" class=\"edge\">\n",
       "<title>response_probs&#45;&gt;response</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M372.93,-83.57C302.76,-91.16 191.59,-105.88 155,-126 140.71,-133.86 127.73,-145.53 116.77,-157.61\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"114.4,-154.99 110.5,-164.84 119.69,-159.57 114.4,-154.99\"/>\n",
       "</g>\n",
       "</g>\n",
       "</svg>\n"
      ],
      "text/plain": [
       "<graphviz.graphs.Digraph at 0x7f33f04e9760>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "GENDER_ID, GENDER = pd.factorize([\"M\" if m else \"F\" for m in TROLLEY.male.values])\n",
    "\n",
    "coords = {\"GENDER\": GENDER, \"CUTPOINTS\": CUTPOINTS}\n",
    "with pm.Model(coords=coords) as total_effect_gender_model:\n",
    "\n",
    "    # Data for posetior predictions\n",
    "    action = pm.Data(\"action\", TROLLEY.action.astype(float))\n",
    "    intent = pm.Data(\"intent\", TROLLEY.intention.astype(float))\n",
    "    contact = pm.Data(\"contact\", TROLLEY.contact.astype(float))\n",
    "    gender = pm.Data(\"gender\", GENDER_ID)\n",
    "\n",
    "    # Priors\n",
    "    beta_action = pm.Normal(\"beta_action\", 0, 0.5, dims=\"GENDER\")\n",
    "    beta_intent = pm.Normal(\"beta_intent\", 0, 0.5, dims=\"GENDER\")\n",
    "    beta_contact = pm.Normal(\"beta_contact\", 0, 0.5, dims=\"GENDER\")\n",
    "\n",
    "    cutpoints = pm.Normal(\n",
    "        \"alpha\",\n",
    "        mu=0,\n",
    "        sigma=1,\n",
    "        transform=pm.distributions.transforms.univariate_ordered,\n",
    "        shape=N_RESPONSE_CLASSES - 1,\n",
    "        initval=np.arange(N_RESPONSE_CLASSES - 1)\n",
    "        - 2.5,  # use ordering (with coarse log-odds centering) for init\n",
    "        dims=\"CUTPOINTS\",\n",
    "    )\n",
    "\n",
    "    # Likelihood\n",
    "    phi = (\n",
    "        beta_action[gender] * action + beta_intent[gender] * intent + beta_contact[gender] * contact\n",
    "    )\n",
    "\n",
    "    pm.OrderedLogistic(\"response\", cutpoints=cutpoints, eta=phi, observed=RESPONSE_ID)\n",
    "\n",
    "pm.model_to_graphviz(total_effect_gender_model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "ae46b6d2",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Initializing NUTS using jitter+adapt_diag...\n",
      "Multiprocess sampling (4 chains in 4 jobs)\n",
      "NUTS: [beta_action, beta_intent, beta_contact, alpha]\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "cca43ddcc62147f7a8e473119a96e572",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Sampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 41 seconds.\n"
     ]
    }
   ],
   "source": [
    "with total_effect_gender_model:\n",
    "    total_effect_gender_inference = pm.sample()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "28466c52",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>sd</th>\n",
       "      <th>hdi_3%</th>\n",
       "      <th>hdi_97%</th>\n",
       "      <th>mcse_mean</th>\n",
       "      <th>mcse_sd</th>\n",
       "      <th>ess_bulk</th>\n",
       "      <th>ess_tail</th>\n",
       "      <th>r_hat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>beta_action[F]</th>\n",
       "      <td>-0.880</td>\n",
       "      <td>0.051</td>\n",
       "      <td>-0.985</td>\n",
       "      <td>-0.791</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.001</td>\n",
       "      <td>3392.0</td>\n",
       "      <td>2771.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>beta_action[M]</th>\n",
       "      <td>-0.527</td>\n",
       "      <td>0.047</td>\n",
       "      <td>-0.618</td>\n",
       "      <td>-0.441</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.001</td>\n",
       "      <td>3353.0</td>\n",
       "      <td>3070.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>beta_intent[F]</th>\n",
       "      <td>-0.895</td>\n",
       "      <td>0.048</td>\n",
       "      <td>-0.981</td>\n",
       "      <td>-0.802</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.001</td>\n",
       "      <td>3386.0</td>\n",
       "      <td>3078.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>beta_intent[M]</th>\n",
       "      <td>-0.554</td>\n",
       "      <td>0.044</td>\n",
       "      <td>-0.634</td>\n",
       "      <td>-0.467</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.001</td>\n",
       "      <td>3778.0</td>\n",
       "      <td>2637.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>beta_contact[F]</th>\n",
       "      <td>-1.059</td>\n",
       "      <td>0.066</td>\n",
       "      <td>-1.181</td>\n",
       "      <td>-0.935</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.001</td>\n",
       "      <td>3581.0</td>\n",
       "      <td>3001.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>beta_contact[M]</th>\n",
       "      <td>-0.834</td>\n",
       "      <td>0.061</td>\n",
       "      <td>-0.948</td>\n",
       "      <td>-0.721</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.001</td>\n",
       "      <td>3350.0</td>\n",
       "      <td>2956.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>alpha[1]</th>\n",
       "      <td>-2.828</td>\n",
       "      <td>0.046</td>\n",
       "      <td>-2.913</td>\n",
       "      <td>-2.740</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.001</td>\n",
       "      <td>2141.0</td>\n",
       "      <td>2476.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>alpha[2]</th>\n",
       "      <td>-2.147</td>\n",
       "      <td>0.041</td>\n",
       "      <td>-2.221</td>\n",
       "      <td>-2.069</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.001</td>\n",
       "      <td>2068.0</td>\n",
       "      <td>2628.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>alpha[3]</th>\n",
       "      <td>-1.562</td>\n",
       "      <td>0.038</td>\n",
       "      <td>-1.631</td>\n",
       "      <td>-1.489</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.001</td>\n",
       "      <td>2153.0</td>\n",
       "      <td>2425.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>alpha[4]</th>\n",
       "      <td>-0.530</td>\n",
       "      <td>0.036</td>\n",
       "      <td>-0.599</td>\n",
       "      <td>-0.466</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.000</td>\n",
       "      <td>2609.0</td>\n",
       "      <td>2927.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>alpha[5]</th>\n",
       "      <td>0.145</td>\n",
       "      <td>0.035</td>\n",
       "      <td>0.077</td>\n",
       "      <td>0.207</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.000</td>\n",
       "      <td>2808.0</td>\n",
       "      <td>3497.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>alpha[6]</th>\n",
       "      <td>1.059</td>\n",
       "      <td>0.039</td>\n",
       "      <td>0.990</td>\n",
       "      <td>1.134</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.000</td>\n",
       "      <td>3410.0</td>\n",
       "      <td>3540.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  mean     sd  hdi_3%  hdi_97%  mcse_mean  mcse_sd  ess_bulk  \\\n",
       "beta_action[F]  -0.880  0.051  -0.985   -0.791      0.001    0.001    3392.0   \n",
       "beta_action[M]  -0.527  0.047  -0.618   -0.441      0.001    0.001    3353.0   \n",
       "beta_intent[F]  -0.895  0.048  -0.981   -0.802      0.001    0.001    3386.0   \n",
       "beta_intent[M]  -0.554  0.044  -0.634   -0.467      0.001    0.001    3778.0   \n",
       "beta_contact[F] -1.059  0.066  -1.181   -0.935      0.001    0.001    3581.0   \n",
       "beta_contact[M] -0.834  0.061  -0.948   -0.721      0.001    0.001    3350.0   \n",
       "alpha[1]        -2.828  0.046  -2.913   -2.740      0.001    0.001    2141.0   \n",
       "alpha[2]        -2.147  0.041  -2.221   -2.069      0.001    0.001    2068.0   \n",
       "alpha[3]        -1.562  0.038  -1.631   -1.489      0.001    0.001    2153.0   \n",
       "alpha[4]        -0.530  0.036  -0.599   -0.466      0.001    0.000    2609.0   \n",
       "alpha[5]         0.145  0.035   0.077    0.207      0.001    0.000    2808.0   \n",
       "alpha[6]         1.059  0.039   0.990    1.134      0.001    0.000    3410.0   \n",
       "\n",
       "                 ess_tail  r_hat  \n",
       "beta_action[F]     2771.0    1.0  \n",
       "beta_action[M]     3070.0    1.0  \n",
       "beta_intent[F]     3078.0    1.0  \n",
       "beta_intent[M]     2637.0    1.0  \n",
       "beta_contact[F]    3001.0    1.0  \n",
       "beta_contact[M]    2956.0    1.0  \n",
       "alpha[1]           2476.0    1.0  \n",
       "alpha[2]           2628.0    1.0  \n",
       "alpha[3]           2425.0    1.0  \n",
       "alpha[4]           2927.0    1.0  \n",
       "alpha[5]           3497.0    1.0  \n",
       "alpha[6]           3540.0    1.0  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "az.summary(\n",
    "    total_effect_gender_inference, var_names=[\"beta_action\", \"beta_intent\", \"beta_contact\", \"alpha\"]\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d351377c",
   "metadata": {},
   "source": [
    "### Running Counterfactuals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "d4b7e762",
   "metadata": {},
   "outputs": [],
   "source": [
    "def run_total_gender_counterfactual(action=0, intent=0, contact=0, gender=0, ax=None):\n",
    "    with total_effect_gender_model:\n",
    "        # hrmm, xr.DataArray doesn't seem to facilitate / speed up predictions 🤔\n",
    "        pm.set_data(\n",
    "            {\n",
    "                \"action\": xr.DataArray([action] * N_TROLLEY_RESPONSES),\n",
    "                \"intent\": xr.DataArray([intent] * N_TROLLEY_RESPONSES),\n",
    "                \"contact\": xr.DataArray([contact] * N_TROLLEY_RESPONSES),\n",
    "                \"gender\": xr.DataArray([gender] * N_TROLLEY_RESPONSES),\n",
    "            }\n",
    "        )\n",
    "        ppd = pm.sample_posterior_predictive(\n",
    "            total_effect_gender_inference, extend_inferencedata=False\n",
    "        )\n",
    "\n",
    "    # recode back to 1-7\n",
    "    posterior_predictive = ppd.posterior_predictive[\"response\"] + 1\n",
    "\n",
    "    # Posterior predictive density. Due to idiosyncracies with plt.hist(),\n",
    "    # az.plot_density() refuses to center on integer ticks, so we just do\n",
    "    # it manually with numpy and plt.bar()\n",
    "    if ax is not None:\n",
    "        plt.sca(ax)\n",
    "    else:\n",
    "        plt.subplots(figsize=(6, 3))\n",
    "    counts, bins = np.histogram(posterior_predictive.values.ravel(), bins=np.arange(1, 9))\n",
    "    total_counts = counts.sum()\n",
    "    counts = [c / total_counts for c in counts]\n",
    "    plt.bar(bins[:-1], counts, width=0.25)\n",
    "\n",
    "    plt.xlim([0.5, 7.5])\n",
    "    plt.xlabel(\"response\")\n",
    "    plt.ylabel(\"density\")\n",
    "    plt.title(f\"action={action}; intent={intent}; contact={contact}; gender={gender}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "a68b2300",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Sampling: [response]\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "9d43fa1992cc4c2b88cedc29e1e62a76",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 600x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "run_total_gender_counterfactual(action=0, intent=1, contact=1, gender=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "c403d91d-61f8-4565-9b42-b0c3d6960a8d",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Sampling: [response]\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "4608d94ee1274b7bb932be2d0a5e608f",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 600x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "run_total_gender_counterfactual(action=0, intent=1, contact=1, gender=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ff77f463",
   "metadata": {},
   "source": [
    "## Hang on!  This is a <span style=\"color:blue\">voluntary</span> sample."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "c318e170",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/svg+xml": [
       "<?xml version=\"1.0\" encoding=\"UTF-8\" standalone=\"no\"?>\n",
       "<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n",
       " \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\n",
       "<!-- Generated by graphviz version 12.0.0 (20240803.0821)\n",
       " -->\n",
       "<!-- Pages: 1 -->\n",
       "<svg width=\"674pt\" height=\"294pt\"\n",
       " viewBox=\"0.00 0.00 673.97 293.64\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n",
       "<g id=\"graph0\" class=\"graph\" transform=\"scale(1 1) rotate(0) translate(4 289.64)\">\n",
       "<polygon fill=\"white\" stroke=\"none\" points=\"-4,4 -4,-289.64 669.97,-289.64 669.97,4 -4,4\"/>\n",
       "<!-- X -->\n",
       "<g id=\"node1\" class=\"node\">\n",
       "<title>X</title>\n",
       "<ellipse fill=\"none\" stroke=\"black\" cx=\"315.48\" cy=\"-172.2\" rx=\"145.49\" ry=\"19.45\"/>\n",
       "<text text-anchor=\"middle\" x=\"315.48\" y=\"-168.65\" font-family=\"Times,serif\" font-size=\"14.00\">action, intenition, contact, X</text>\n",
       "</g>\n",
       "<!-- R -->\n",
       "<g id=\"node2\" class=\"node\">\n",
       "<title>R</title>\n",
       "<ellipse fill=\"none\" stroke=\"red\" cx=\"581.47\" cy=\"-172.2\" rx=\"64.88\" ry=\"19.45\"/>\n",
       "<text text-anchor=\"middle\" x=\"581.47\" y=\"-168.65\" font-family=\"Times,serif\" font-size=\"14.00\">response, R</text>\n",
       "</g>\n",
       "<!-- X&#45;&gt;R -->\n",
       "<g id=\"edge1\" class=\"edge\">\n",
       "<title>X&#45;&gt;R</title>\n",
       "<path fill=\"none\" stroke=\"red\" d=\"M461.22,-172.2C476.23,-172.2 491.02,-172.2 504.8,-172.2\"/>\n",
       "<polygon fill=\"red\" stroke=\"red\" points=\"504.61,-175.7 514.61,-172.2 504.61,-168.7 504.61,-175.7\"/>\n",
       "</g>\n",
       "<!-- S -->\n",
       "<g id=\"node3\" class=\"node\">\n",
       "<title>S</title>\n",
       "<ellipse fill=\"none\" stroke=\"black\" cx=\"315.48\" cy=\"-266.2\" rx=\"45.25\" ry=\"19.45\"/>\n",
       "<text text-anchor=\"middle\" x=\"315.48\" y=\"-262.65\" font-family=\"Times,serif\" font-size=\"14.00\">story, S</text>\n",
       "</g>\n",
       "<!-- S&#45;&gt;R -->\n",
       "<g id=\"edge2\" class=\"edge\">\n",
       "<title>S&#45;&gt;R</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M358.37,-259.47C387.64,-254.08 427.35,-245.4 460.97,-233.2 488.88,-223.06 518.63,-207.9 541.46,-195.22\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"543.12,-198.3 550.11,-190.34 539.68,-192.21 543.12,-198.3\"/>\n",
       "</g>\n",
       "<!-- E -->\n",
       "<g id=\"node4\" class=\"node\">\n",
       "<title>E</title>\n",
       "<ellipse fill=\"none\" stroke=\"black\" cx=\"315.48\" cy=\"-40.2\" rx=\"70.18\" ry=\"19.45\"/>\n",
       "<text text-anchor=\"middle\" x=\"315.48\" y=\"-36.65\" font-family=\"Times,serif\" font-size=\"14.00\">education, E</text>\n",
       "</g>\n",
       "<!-- E&#45;&gt;R -->\n",
       "<g id=\"edge3\" class=\"edge\">\n",
       "<title>E&#45;&gt;R</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M380.17,-48.14C406.16,-53.04 435.93,-60.85 460.97,-73.2 497.61,-91.27 532.77,-122.66 555.35,-145.26\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"552.8,-147.66 562.3,-152.36 557.81,-142.77 552.8,-147.66\"/>\n",
       "</g>\n",
       "<!-- P -->\n",
       "<g id=\"node7\" class=\"node\">\n",
       "<title>P</title>\n",
       "<ellipse fill=\"none\" stroke=\"blue\" stroke-dasharray=\"5,2\" cx=\"581.47\" cy=\"-40.2\" rx=\"84.5\" ry=\"19.45\"/>\n",
       "<text text-anchor=\"middle\" x=\"581.47\" y=\"-36.65\" font-family=\"Times,serif\" font-size=\"14.00\">participation, P</text>\n",
       "</g>\n",
       "<!-- E&#45;&gt;P -->\n",
       "<g id=\"edge8\" class=\"edge\">\n",
       "<title>E&#45;&gt;P</title>\n",
       "<path fill=\"none\" stroke=\"blue\" d=\"M385.84,-40.2C416.18,-40.2 452.26,-40.2 485.07,-40.2\"/>\n",
       "<polygon fill=\"blue\" stroke=\"blue\" points=\"485.01,-43.7 495.01,-40.2 485.01,-36.7 485.01,-43.7\"/>\n",
       "</g>\n",
       "<!-- Y -->\n",
       "<g id=\"node5\" class=\"node\">\n",
       "<title>Y</title>\n",
       "<ellipse fill=\"none\" stroke=\"black\" cx=\"67\" cy=\"-125.2\" rx=\"39.42\" ry=\"19.45\"/>\n",
       "<text text-anchor=\"middle\" x=\"67\" y=\"-121.65\" font-family=\"Times,serif\" font-size=\"14.00\">age, Y</text>\n",
       "</g>\n",
       "<!-- Y&#45;&gt;R -->\n",
       "<g id=\"edge4\" class=\"edge\">\n",
       "<title>Y&#45;&gt;R</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M100.64,-135.83C111.88,-140.42 124.11,-146.52 134,-154.2 154.5,-170.12 146.66,-188.83 170,-200.2 288.05,-257.72 448.25,-218.18 529.17,-191.32\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"530.27,-194.64 538.62,-188.11 528.02,-188.01 530.27,-194.64\"/>\n",
       "</g>\n",
       "<!-- Y&#45;&gt;E -->\n",
       "<g id=\"edge6\" class=\"edge\">\n",
       "<title>Y&#45;&gt;E</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M99.97,-114.17C140.42,-100.22 210.7,-75.98 259.76,-59.07\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"260.76,-62.43 269.07,-55.86 258.48,-55.81 260.76,-62.43\"/>\n",
       "</g>\n",
       "<!-- Y&#45;&gt;P -->\n",
       "<g id=\"edge9\" class=\"edge\">\n",
       "<title>Y&#45;&gt;P</title>\n",
       "<path fill=\"none\" stroke=\"blue\" d=\"M105.84,-120.81C176.12,-112.42 331.43,-92.72 460.97,-68.2 477.72,-65.03 495.66,-61.1 512.42,-57.21\"/>\n",
       "<polygon fill=\"blue\" stroke=\"blue\" points=\"512.84,-60.71 521.77,-55.01 511.23,-53.89 512.84,-60.71\"/>\n",
       "</g>\n",
       "<!-- G -->\n",
       "<g id=\"node6\" class=\"node\">\n",
       "<title>G</title>\n",
       "<ellipse fill=\"none\" stroke=\"black\" cx=\"67\" cy=\"-40.2\" rx=\"54.8\" ry=\"19.45\"/>\n",
       "<text text-anchor=\"middle\" x=\"67\" y=\"-36.65\" font-family=\"Times,serif\" font-size=\"14.00\">gender, G</text>\n",
       "</g>\n",
       "<!-- G&#45;&gt;R -->\n",
       "<g id=\"edge5\" class=\"edge\">\n",
       "<title>G&#45;&gt;R</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M110.63,-52.1C128.89,-57.15 150.47,-63.04 170,-68.2 293.89,-100.89 439.36,-137.28 519.59,-157.17\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"518.45,-160.5 529,-159.5 520.14,-153.7 518.45,-160.5\"/>\n",
       "</g>\n",
       "<!-- G&#45;&gt;E -->\n",
       "<g id=\"edge7\" class=\"edge\">\n",
       "<title>G&#45;&gt;E</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M122.09,-40.2C154.84,-40.2 197.22,-40.2 233.87,-40.2\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"233.52,-43.7 243.52,-40.2 233.52,-36.7 233.52,-43.7\"/>\n",
       "</g>\n",
       "<!-- G&#45;&gt;P -->\n",
       "<g id=\"edge10\" class=\"edge\">\n",
       "<title>G&#45;&gt;P</title>\n",
       "<path fill=\"none\" stroke=\"blue\" d=\"M107.21,-26.79C125.96,-21.06 148.89,-15.04 170,-12.2 298.16,5.07 332.5,2.65 460.97,-12.2 479.03,-14.28 498.32,-18.09 516.02,-22.24\"/>\n",
       "<polygon fill=\"blue\" stroke=\"blue\" points=\"515.08,-25.61 525.63,-24.57 516.73,-18.81 515.08,-25.61\"/>\n",
       "</g>\n",
       "<!-- unobserved -->\n",
       "<g id=\"node8\" class=\"node\">\n",
       "<title>unobserved</title>\n",
       "<ellipse fill=\"none\" stroke=\"black\" stroke-dasharray=\"5,2\" cx=\"67\" cy=\"-182.2\" rx=\"67\" ry=\"19.45\"/>\n",
       "<text text-anchor=\"middle\" x=\"67\" y=\"-178.65\" font-family=\"Times,serif\" font-size=\"14.00\">unobserved</text>\n",
       "</g>\n",
       "</g>\n",
       "</svg>\n"
      ],
      "text/plain": [
       "<graphviz.graphs.Digraph at 0x7f33ea31cb00>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "utils.draw_causal_graph(\n",
    "    edge_list=[\n",
    "        (\"X\", \"R\"),\n",
    "        (\"S\", \"R\"),\n",
    "        (\"E\", \"R\"),\n",
    "        (\"Y\", \"R\"),\n",
    "        (\"G\", \"R\"),\n",
    "        (\"Y\", \"E\"),\n",
    "        (\"G\", \"E\"),\n",
    "        (\"E\", \"P\"),\n",
    "        (\"Y\", \"P\"),\n",
    "        (\"G\", \"P\"),\n",
    "    ],\n",
    "    node_props={\n",
    "        \"X\": {\"label\": \"action, intenition, contact, X\"},\n",
    "        \"R\": {\"color\": \"red\", \"label\": \"response, R\"},\n",
    "        \"S\": {\"label\": \"story, S\"},\n",
    "        \"E\": {\"label\": \"education, E\"},\n",
    "        \"Y\": {\"label\": \"age, Y\"},\n",
    "        \"G\": {\"label\": \"gender, G\"},\n",
    "        \"P\": {\"label\": \"participation, P\", \"color\": \"blue\", \"style\": \"dashed\"},\n",
    "        \"unobserved\": {\"style\": \"dashed\"},\n",
    "    },\n",
    "    edge_props={\n",
    "        (\"E\", \"P\"): {\"color\": \"blue\"},\n",
    "        (\"Y\", \"P\"): {\"color\": \"blue\"},\n",
    "        (\"G\", \"P\"): {\"color\": \"blue\"},\n",
    "        (\"X\", \"R\"): {\"color\": \"red\"},\n",
    "    },\n",
    "    graph_direction=\"LR\",\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fa9f70c2",
   "metadata": {},
   "source": [
    "### Voluntary Samples, Participation, and Endogenous Selection\n",
    "- Age, Education, and Gender all contribute to an unmeasured variable Participation\n",
    "- Participation is a collider: conditioning causes, E, Y, G to covary\n",
    "- **Not actually possible to estimate the Total Effect of Gender**\n",
    "  - We **CAN estimate the _Direct Effect_ of Gender $G$ by stratifying by Education $E$ and Age $Y$**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eb7b43e8-0d7d-4d55-a555-f03332aded66",
   "metadata": {},
   "source": [
    "#### Looking at the distribution of Education and Age in the sample"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "c64f8ecf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x300 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "_, axs = plt.subplots(1, 2, figsize=(10, 3), gridspec_kw={\"width_ratios\": [2, 4]})\n",
    "\n",
    "EDUCATION_ORDERED_LIST = [\n",
    "    \"Elementary School\",\n",
    "    \"Middle School\",\n",
    "    \"Some High School\",\n",
    "    \"High School Graduate\",\n",
    "    \"Some College\",\n",
    "    \"Bachelor's Degree\",\n",
    "    \"Master's Degree\",\n",
    "    \"Graduate Degree\",\n",
    "]\n",
    "EDUCUATION_MAP = {edu: ii + 1 for ii, edu in enumerate(EDUCATION_ORDERED_LIST)}\n",
    "EDUCUATION_MAP_R = {v: k for k, v in EDUCUATION_MAP.items()}\n",
    "TROLLEY[\"education_level\"] = TROLLEY.edu.map(EDUCUATION_MAP)\n",
    "TROLLEY[\"education\"] = TROLLEY.education_level.map(EDUCUATION_MAP_R)\n",
    "\n",
    "\n",
    "edu = TROLLEY.groupby(\"edu\").count()[[\"case\"]].rename(columns={\"case\": \"count\"})\n",
    "edu.reset_index(inplace=True)\n",
    "edu[\"edu_level\"] = edu[\"edu\"].map(EDUCUATION_MAP)\n",
    "\n",
    "edu.sort_values(\"edu_level\", inplace=True)\n",
    "\n",
    "plt.sca(axs[0])\n",
    "plt.bar(edu[\"edu_level\"], edu[\"count\"], width=0.25)\n",
    "plt.xlabel(\"education level (ordered)\")\n",
    "plt.ylabel(\"frequency\")\n",
    "plt.title(\"education distribution\")\n",
    "\n",
    "plt.sca(axs[1])\n",
    "age_counts = TROLLEY.groupby(\"age\").count()\n",
    "plt.bar(age_counts.index, age_counts.case, color=\"C1\", width=0.8)\n",
    "plt.xlabel(\"age (years)\")\n",
    "plt.title(\"age distribution\");"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "75382f7d",
   "metadata": {},
   "source": [
    "The observation that data sample's distribution of Education and Age are not aligned with the population's distribution provides evidence that these variables are likely confounding factors associated with participation."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1ff4dbfa",
   "metadata": {},
   "source": [
    "## Ordered Monotonic Predictors\n",
    "\n",
    "Similar to the Response outcome, **Education** is also an **Ordered category**.\n",
    "\n",
    "- unlikely that each level has the same effect on participation/response\n",
    "- we would like a parameter for each level, while **enforcing ordering** so that each successive level has a larger magnitude effect than the previous.\n",
    "\n",
    "For each level of education:\n",
    "1. (Elementary School) $\\rightarrow \\phi_i = 0$\n",
    "2. (Middle School) $\\rightarrow \\phi_i = \\delta_1$\n",
    "3. (Some High School) $\\rightarrow \\phi_i = \\delta_1 + \\delta_2$\n",
    "4. (High School Graduate) $\\rightarrow \\phi_i = \\delta_1 + \\delta_2 + \\delta_3$\n",
    "5. (Some College) $\\rightarrow \\phi_i = \\delta_1 + \\delta_2 + \\delta_3 + \\delta_4$\n",
    "6. (College Graduate) $\\rightarrow \\phi_i = \\delta_1 + \\delta_2 + \\delta_3 + \\delta_4 + \\delta_5$\n",
    "7. (Master's Degreee) $\\rightarrow \\phi_i = \\delta_1 + \\delta_2 + \\delta_3 + \\delta_4 + \\delta_5 + \\delta_6$\n",
    "8. (Graduate Degreee) $\\rightarrow \\phi_i = \\delta_1 + \\delta_2 + \\delta_3 + \\delta_4 + \\delta_5 + \\delta_6+ \\delta_7 = \\beta_E$\n",
    "\n",
    "where $\\beta_E$ is the maximum effect of education. We thus break down the maximum effect into a convex combination of education terms.\n",
    "\n",
    "$$\n",
    "\\begin{align*}\n",
    "\\delta_0 &= 0 \\\\\n",
    "\\phi_i &= \\sum_{j=0}^{E_i-1} \\delta_j = 1\n",
    "\\end{align*}\n",
    "$$\n",
    "\n",
    "### Ordered Monotonic Priors\n",
    "\n",
    "- $\\delta$ parameters form a Simplex--a vector of proportions and sum to 1\n",
    "- The simplex parameter space is modeled by a **Dirichlet distribution**\n",
    "  - Dirichlet gives us a distribution over distributions\n",
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "421b2a70-0647-4cbb-89ec-83dbd1b8cc72",
   "metadata": {},
   "source": [
    "#### Demonstrating the parameterization of the Dirichlet distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "75743120",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 500x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 500x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 500x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_dirichlet(alpha, n_samples=100, ax=None, random_seed=123):\n",
    "    \"\"\"\n",
    "    `alpha` is a vector of relative parameter importances. Larger\n",
    "    values make the distribution more concentrated over the mean\n",
    "    proportion.\n",
    "    \"\"\"\n",
    "    np.random.seed(random_seed)\n",
    "    if ax is not None:\n",
    "        plt.sca(ax)\n",
    "    else:\n",
    "        _, ax = plt.subplots(figsize=(5, 4))\n",
    "    dirichlet = stats.dirichlet(alpha)\n",
    "    dirichlet_samples = dirichlet.rvs(size=n_samples).T\n",
    "    plt.plot(dirichlet_samples, alpha=0.1, color=\"gray\", linewidth=2)\n",
    "    plt.plot(\n",
    "        np.arange(len(alpha)),\n",
    "        dirichlet_samples[:, 0],\n",
    "        color=\"C0\",\n",
    "        linewidth=3,\n",
    "        label=\"single sample\",\n",
    "    )\n",
    "    plt.scatter(np.arange(len(alpha)), dirichlet_samples[:, 0], color=\"C0\")\n",
    "\n",
    "    plt.xticks(np.arange(len(alpha)))\n",
    "    plt.xlabel(\"Parameter Index\")\n",
    "    plt.ylim([0, 1])\n",
    "    plt.ylabel(\"probability\")\n",
    "    plt.title(f\"$\\\\alpha={list(alpha)}$\")\n",
    "    plt.legend()\n",
    "\n",
    "\n",
    "n_ordered_categories = 8\n",
    "dirichlet_alphas = [\n",
    "    np.ones(n_ordered_categories).astype(int),\n",
    "    np.ones(n_ordered_categories).astype(int) * 10,\n",
    "    np.round((1 + np.arange(4) ** 2), 1),\n",
    "]\n",
    "\n",
    "for alphas in dirichlet_alphas:\n",
    "    plot_dirichlet(alphas)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aef175eb",
   "metadata": {},
   "source": [
    "### Assessing the *Direct Effect* of Education: Stratifying by Gender & Age\n",
    "\n",
    "McElreath builds a model for the **Total Effect** of gender in the lecture, but then points out that due to the backdoor path caused by gender (via participation), we can't interpret total cause of education. We thus need need to **also stratify by gender**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "99978d8d-9f1e-4df7-ae44-6c5290ffa23d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/svg+xml": [
       "<?xml version=\"1.0\" encoding=\"UTF-8\" standalone=\"no\"?>\n",
       "<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n",
       " \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\n",
       "<!-- Generated by graphviz version 12.0.0 (20240803.0821)\n",
       " -->\n",
       "<!-- Pages: 1 -->\n",
       "<svg width=\"1771pt\" height=\"425pt\"\n",
       " viewBox=\"0.00 0.00 1771.00 425.09\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n",
       "<g id=\"graph0\" class=\"graph\" transform=\"scale(1 1) rotate(0) translate(4 421.09)\">\n",
       "<polygon fill=\"white\" stroke=\"none\" points=\"-4,4 -4,-421.09 1767,-421.09 1767,4 -4,4\"/>\n",
       "<g id=\"clust1\" class=\"cluster\">\n",
       "<title>cluster9930</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M20,-126C20,-126 669,-126 669,-126 675,-126 681,-132 681,-138 681,-138 681,-251.55 681,-251.55 681,-257.55 675,-263.55 669,-263.55 669,-263.55 20,-263.55 20,-263.55 14,-263.55 8,-257.55 8,-251.55 8,-251.55 8,-138 8,-138 8,-132 14,-126 20,-126\"/>\n",
       "<text text-anchor=\"middle\" x=\"658\" y=\"-136.2\" font-family=\"Times,serif\" font-size=\"14.00\">9930</text>\n",
       "</g>\n",
       "<g id=\"clust2\" class=\"cluster\">\n",
       "<title>clusterGENDER (2)</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M701,-126C701,-126 1446,-126 1446,-126 1452,-126 1458,-132 1458,-138 1458,-138 1458,-251.55 1458,-251.55 1458,-257.55 1452,-263.55 1446,-263.55 1446,-263.55 701,-263.55 701,-263.55 695,-263.55 689,-257.55 689,-251.55 689,-251.55 689,-138 689,-138 689,-132 695,-126 701,-126\"/>\n",
       "<text text-anchor=\"middle\" x=\"1411\" y=\"-136.2\" font-family=\"Times,serif\" font-size=\"14.00\">GENDER (2)</text>\n",
       "</g>\n",
       "<g id=\"clust3\" class=\"cluster\">\n",
       "<title>cluster7</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M1487,-271.55C1487,-271.55 1585,-271.55 1585,-271.55 1591,-271.55 1597,-277.55 1597,-283.55 1597,-283.55 1597,-397.09 1597,-397.09 1597,-403.09 1591,-409.09 1585,-409.09 1585,-409.09 1487,-409.09 1487,-409.09 1481,-409.09 1475,-403.09 1475,-397.09 1475,-397.09 1475,-283.55 1475,-283.55 1475,-277.55 1481,-271.55 1487,-271.55\"/>\n",
       "<text text-anchor=\"middle\" x=\"1585.25\" y=\"-281.75\" font-family=\"Times,serif\" font-size=\"14.00\">7</text>\n",
       "</g>\n",
       "<g id=\"clust4\" class=\"cluster\">\n",
       "<title>clusterEDUCATION_LEVEL (8)</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M1478,-139.77C1478,-139.77 1623,-139.77 1623,-139.77 1629,-139.77 1635,-145.77 1635,-151.77 1635,-151.77 1635,-237.77 1635,-237.77 1635,-243.77 1629,-249.77 1623,-249.77 1623,-249.77 1478,-249.77 1478,-249.77 1472,-249.77 1466,-243.77 1466,-237.77 1466,-237.77 1466,-151.77 1466,-151.77 1466,-145.77 1472,-139.77 1478,-139.77\"/>\n",
       "<text text-anchor=\"middle\" x=\"1550.5\" y=\"-149.97\" font-family=\"Times,serif\" font-size=\"14.00\">EDUCATION_LEVEL (8)</text>\n",
       "</g>\n",
       "<g id=\"clust5\" class=\"cluster\">\n",
       "<title>clusterCUTPOINTS (6)</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M1655,-126C1655,-126 1743,-126 1743,-126 1749,-126 1755,-132 1755,-138 1755,-138 1755,-251.55 1755,-251.55 1755,-257.55 1749,-263.55 1743,-263.55 1743,-263.55 1655,-263.55 1655,-263.55 1649,-263.55 1643,-257.55 1643,-251.55 1643,-251.55 1643,-138 1643,-138 1643,-132 1649,-126 1655,-126\"/>\n",
       "<text text-anchor=\"middle\" x=\"1698.62\" y=\"-136.2\" font-family=\"Times,serif\" font-size=\"14.00\">CUTPOINTS (6)</text>\n",
       "</g>\n",
       "<g id=\"clust6\" class=\"cluster\">\n",
       "<title>cluster9930 x 7</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M634,-8C634,-8 746,-8 746,-8 752,-8 758,-14 758,-20 758,-20 758,-106 758,-106 758,-112 752,-118 746,-118 746,-118 634,-118 634,-118 628,-118 622,-112 622,-106 622,-106 622,-20 622,-20 622,-14 628,-8 634,-8\"/>\n",
       "<text text-anchor=\"middle\" x=\"723.38\" y=\"-18.2\" font-family=\"Times,serif\" font-size=\"14.00\">9930 x 7</text>\n",
       "</g>\n",
       "<!-- age -->\n",
       "<g id=\"node1\" class=\"node\">\n",
       "<title>age</title>\n",
       "<path fill=\"lightgrey\" stroke=\"black\" d=\"M58,-241.77C58,-241.77 28,-241.77 28,-241.77 22,-241.77 16,-235.77 16,-229.77 16,-229.77 16,-187.27 16,-187.27 16,-181.27 22,-175.27 28,-175.27 28,-175.27 58,-175.27 58,-175.27 64,-175.27 70,-181.27 70,-187.27 70,-187.27 70,-229.77 70,-229.77 70,-235.77 64,-241.77 58,-241.77\"/>\n",
       "<text text-anchor=\"middle\" x=\"43\" y=\"-224.47\" font-family=\"Times,serif\" font-size=\"14.00\">age</text>\n",
       "<text text-anchor=\"middle\" x=\"43\" y=\"-204.97\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
       "<text text-anchor=\"middle\" x=\"43\" y=\"-185.47\" font-family=\"Times,serif\" font-size=\"14.00\">Data</text>\n",
       "</g>\n",
       "<!-- response_probs -->\n",
       "<g id=\"node16\" class=\"node\">\n",
       "<title>response_probs</title>\n",
       "<polygon fill=\"none\" stroke=\"black\" points=\"749.75,-110 630.25,-110 630.25,-43.5 749.75,-43.5 749.75,-110\"/>\n",
       "<text text-anchor=\"middle\" x=\"690\" y=\"-92.7\" font-family=\"Times,serif\" font-size=\"14.00\">response_probs</text>\n",
       "<text text-anchor=\"middle\" x=\"690\" y=\"-73.2\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
       "<text text-anchor=\"middle\" x=\"690\" y=\"-53.7\" font-family=\"Times,serif\" font-size=\"14.00\">Deterministic</text>\n",
       "</g>\n",
       "<!-- age&#45;&gt;response_probs -->\n",
       "<g id=\"edge3\" class=\"edge\">\n",
       "<title>age&#45;&gt;response_probs</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M49,-174.94C53.99,-157.52 62.97,-137.37 79,-126 122.11,-95.42 466.39,-83.24 618.35,-79.32\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"618.4,-82.82 628.31,-79.07 618.23,-75.83 618.4,-82.82\"/>\n",
       "</g>\n",
       "<!-- response -->\n",
       "<g id=\"node2\" class=\"node\">\n",
       "<title>response</title>\n",
       "<ellipse fill=\"lightgrey\" stroke=\"black\" cx=\"153\" cy=\"-208.52\" rx=\"64.88\" ry=\"47.02\"/>\n",
       "<text text-anchor=\"middle\" x=\"153\" y=\"-224.47\" font-family=\"Times,serif\" font-size=\"14.00\">response</text>\n",
       "<text text-anchor=\"middle\" x=\"153\" y=\"-204.97\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
       "<text text-anchor=\"middle\" x=\"153\" y=\"-185.47\" font-family=\"Times,serif\" font-size=\"14.00\">Categorical</text>\n",
       "</g>\n",
       "<!-- contact -->\n",
       "<g id=\"node3\" class=\"node\">\n",
       "<title>contact</title>\n",
       "<path fill=\"lightgrey\" stroke=\"black\" d=\"M289.75,-241.77C289.75,-241.77 248.25,-241.77 248.25,-241.77 242.25,-241.77 236.25,-235.77 236.25,-229.77 236.25,-229.77 236.25,-187.27 236.25,-187.27 236.25,-181.27 242.25,-175.27 248.25,-175.27 248.25,-175.27 289.75,-175.27 289.75,-175.27 295.75,-175.27 301.75,-181.27 301.75,-187.27 301.75,-187.27 301.75,-229.77 301.75,-229.77 301.75,-235.77 295.75,-241.77 289.75,-241.77\"/>\n",
       "<text text-anchor=\"middle\" x=\"269\" y=\"-224.47\" font-family=\"Times,serif\" font-size=\"14.00\">contact</text>\n",
       "<text text-anchor=\"middle\" x=\"269\" y=\"-204.97\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
       "<text text-anchor=\"middle\" x=\"269\" y=\"-185.47\" font-family=\"Times,serif\" font-size=\"14.00\">Data</text>\n",
       "</g>\n",
       "<!-- contact&#45;&gt;response_probs -->\n",
       "<g id=\"edge4\" class=\"edge\">\n",
       "<title>contact&#45;&gt;response_probs</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M277.63,-175.12C283.97,-157.75 294.44,-137.6 311,-126 359.23,-92.23 522.84,-82.09 618.6,-79.05\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"618.53,-82.56 628.42,-78.76 618.32,-75.56 618.53,-82.56\"/>\n",
       "</g>\n",
       "<!-- action -->\n",
       "<g id=\"node4\" class=\"node\">\n",
       "<title>action</title>\n",
       "<path fill=\"lightgrey\" stroke=\"black\" d=\"M366,-241.77C366,-241.77 332,-241.77 332,-241.77 326,-241.77 320,-235.77 320,-229.77 320,-229.77 320,-187.27 320,-187.27 320,-181.27 326,-175.27 332,-175.27 332,-175.27 366,-175.27 366,-175.27 372,-175.27 378,-181.27 378,-187.27 378,-187.27 378,-229.77 378,-229.77 378,-235.77 372,-241.77 366,-241.77\"/>\n",
       "<text text-anchor=\"middle\" x=\"349\" y=\"-224.47\" font-family=\"Times,serif\" font-size=\"14.00\">action</text>\n",
       "<text text-anchor=\"middle\" x=\"349\" y=\"-204.97\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
       "<text text-anchor=\"middle\" x=\"349\" y=\"-185.47\" font-family=\"Times,serif\" font-size=\"14.00\">Data</text>\n",
       "</g>\n",
       "<!-- action&#45;&gt;response_probs -->\n",
       "<g id=\"edge7\" class=\"edge\">\n",
       "<title>action&#45;&gt;response_probs</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M356.29,-174.97C361.84,-157.78 371.29,-137.85 387,-126 422.57,-99.17 541.02,-86.8 618.85,-81.45\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"618.77,-84.97 628.52,-80.81 618.31,-77.98 618.77,-84.97\"/>\n",
       "</g>\n",
       "<!-- gender -->\n",
       "<g id=\"node5\" class=\"node\">\n",
       "<title>gender</title>\n",
       "<path fill=\"lightgrey\" stroke=\"black\" d=\"M446.25,-241.77C446.25,-241.77 407.75,-241.77 407.75,-241.77 401.75,-241.77 395.75,-235.77 395.75,-229.77 395.75,-229.77 395.75,-187.27 395.75,-187.27 395.75,-181.27 401.75,-175.27 407.75,-175.27 407.75,-175.27 446.25,-175.27 446.25,-175.27 452.25,-175.27 458.25,-181.27 458.25,-187.27 458.25,-187.27 458.25,-229.77 458.25,-229.77 458.25,-235.77 452.25,-241.77 446.25,-241.77\"/>\n",
       "<text text-anchor=\"middle\" x=\"427\" y=\"-224.47\" font-family=\"Times,serif\" font-size=\"14.00\">gender</text>\n",
       "<text text-anchor=\"middle\" x=\"427\" y=\"-204.97\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
       "<text text-anchor=\"middle\" x=\"427\" y=\"-185.47\" font-family=\"Times,serif\" font-size=\"14.00\">Data</text>\n",
       "</g>\n",
       "<!-- gender&#45;&gt;response_probs -->\n",
       "<g id=\"edge9\" class=\"edge\">\n",
       "<title>gender&#45;&gt;response_probs</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M435.93,-174.86C442.21,-157.85 452.35,-138.13 468,-126 491.29,-107.94 563.05,-94.52 618.75,-86.48\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"619.1,-89.97 628.51,-85.11 618.12,-83.04 619.1,-89.97\"/>\n",
       "</g>\n",
       "<!-- intent -->\n",
       "<g id=\"node6\" class=\"node\">\n",
       "<title>intent</title>\n",
       "<path fill=\"lightgrey\" stroke=\"black\" d=\"M521.25,-241.77C521.25,-241.77 488.75,-241.77 488.75,-241.77 482.75,-241.77 476.75,-235.77 476.75,-229.77 476.75,-229.77 476.75,-187.27 476.75,-187.27 476.75,-181.27 482.75,-175.27 488.75,-175.27 488.75,-175.27 521.25,-175.27 521.25,-175.27 527.25,-175.27 533.25,-181.27 533.25,-187.27 533.25,-187.27 533.25,-229.77 533.25,-229.77 533.25,-235.77 527.25,-241.77 521.25,-241.77\"/>\n",
       "<text text-anchor=\"middle\" x=\"505\" y=\"-224.47\" font-family=\"Times,serif\" font-size=\"14.00\">intent</text>\n",
       "<text text-anchor=\"middle\" x=\"505\" y=\"-204.97\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
       "<text text-anchor=\"middle\" x=\"505\" y=\"-185.47\" font-family=\"Times,serif\" font-size=\"14.00\">Data</text>\n",
       "</g>\n",
       "<!-- intent&#45;&gt;response_probs -->\n",
       "<g id=\"edge10\" class=\"edge\">\n",
       "<title>intent&#45;&gt;response_probs</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M512.91,-175.1C518.51,-158.38 527.64,-138.85 542,-126 563.33,-106.91 592.34,-95.36 619.03,-88.38\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"619.54,-91.86 628.43,-86.1 617.89,-85.05 619.54,-91.86\"/>\n",
       "</g>\n",
       "<!-- education_level -->\n",
       "<g id=\"node7\" class=\"node\">\n",
       "<title>education_level</title>\n",
       "<path fill=\"lightgrey\" stroke=\"black\" d=\"M660.88,-241.77C660.88,-241.77 563.12,-241.77 563.12,-241.77 557.12,-241.77 551.12,-235.77 551.12,-229.77 551.12,-229.77 551.12,-187.27 551.12,-187.27 551.12,-181.27 557.12,-175.27 563.12,-175.27 563.12,-175.27 660.88,-175.27 660.88,-175.27 666.88,-175.27 672.88,-181.27 672.88,-187.27 672.88,-187.27 672.88,-229.77 672.88,-229.77 672.88,-235.77 666.88,-241.77 660.88,-241.77\"/>\n",
       "<text text-anchor=\"middle\" x=\"612\" y=\"-224.47\" font-family=\"Times,serif\" font-size=\"14.00\">education_level</text>\n",
       "<text text-anchor=\"middle\" x=\"612\" y=\"-204.97\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
       "<text text-anchor=\"middle\" x=\"612\" y=\"-185.47\" font-family=\"Times,serif\" font-size=\"14.00\">Data</text>\n",
       "</g>\n",
       "<!-- education_level&#45;&gt;response_probs -->\n",
       "<g id=\"edge12\" class=\"edge\">\n",
       "<title>education_level&#45;&gt;response_probs</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M631.68,-174.78C641.66,-158.18 653.89,-137.82 664.57,-120.05\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"667.39,-122.16 669.54,-111.78 661.39,-118.55 667.39,-122.16\"/>\n",
       "</g>\n",
       "<!-- beta_contact -->\n",
       "<g id=\"node8\" class=\"node\">\n",
       "<title>beta_contact</title>\n",
       "<ellipse fill=\"none\" stroke=\"black\" cx=\"769\" cy=\"-208.52\" rx=\"71.77\" ry=\"47.02\"/>\n",
       "<text text-anchor=\"middle\" x=\"769\" y=\"-224.47\" font-family=\"Times,serif\" font-size=\"14.00\">beta_contact</text>\n",
       "<text text-anchor=\"middle\" x=\"769\" y=\"-204.97\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
       "<text text-anchor=\"middle\" x=\"769\" y=\"-185.47\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
       "</g>\n",
       "<!-- beta_contact&#45;&gt;response_probs -->\n",
       "<g id=\"edge2\" class=\"edge\">\n",
       "<title>beta_contact&#45;&gt;response_probs</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M742.69,-164.31C733.94,-149.93 724.21,-133.95 715.52,-119.67\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"718.67,-118.11 710.48,-111.39 712.69,-121.75 718.67,-118.11\"/>\n",
       "</g>\n",
       "<!-- beta_intent -->\n",
       "<g id=\"node9\" class=\"node\">\n",
       "<title>beta_intent</title>\n",
       "<ellipse fill=\"none\" stroke=\"black\" cx=\"924\" cy=\"-208.52\" rx=\"65.41\" ry=\"47.02\"/>\n",
       "<text text-anchor=\"middle\" x=\"924\" y=\"-224.47\" font-family=\"Times,serif\" font-size=\"14.00\">beta_intent</text>\n",
       "<text text-anchor=\"middle\" x=\"924\" y=\"-204.97\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
       "<text text-anchor=\"middle\" x=\"924\" y=\"-185.47\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
       "</g>\n",
       "<!-- beta_intent&#45;&gt;response_probs -->\n",
       "<g id=\"edge6\" class=\"edge\">\n",
       "<title>beta_intent&#45;&gt;response_probs</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M894.14,-166.24C881.88,-151.76 866.65,-136.51 850,-126 823.14,-109.03 789.98,-97.75 760.83,-90.4\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"761.88,-87.05 751.34,-88.12 760.25,-93.86 761.88,-87.05\"/>\n",
       "</g>\n",
       "<!-- beta_age -->\n",
       "<g id=\"node10\" class=\"node\">\n",
       "<title>beta_age</title>\n",
       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1061\" cy=\"-208.52\" rx=\"53.21\" ry=\"47.02\"/>\n",
       "<text text-anchor=\"middle\" x=\"1061\" y=\"-224.47\" font-family=\"Times,serif\" font-size=\"14.00\">beta_age</text>\n",
       "<text text-anchor=\"middle\" x=\"1061\" y=\"-204.97\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
       "<text text-anchor=\"middle\" x=\"1061\" y=\"-185.47\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
       "</g>\n",
       "<!-- beta_age&#45;&gt;response_probs -->\n",
       "<g id=\"edge8\" class=\"edge\">\n",
       "<title>beta_age&#45;&gt;response_probs</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M1037.97,-165.87C1027.67,-150.99 1014.17,-135.56 998,-126 959.04,-102.95 839.49,-89.43 761.31,-82.82\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"761.85,-79.35 751.59,-82.01 761.27,-86.32 761.85,-79.35\"/>\n",
       "</g>\n",
       "<!-- beta_education -->\n",
       "<g id=\"node11\" class=\"node\">\n",
       "<title>beta_education</title>\n",
       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1216\" cy=\"-208.52\" rx=\"83.97\" ry=\"47.02\"/>\n",
       "<text text-anchor=\"middle\" x=\"1216\" y=\"-224.47\" font-family=\"Times,serif\" font-size=\"14.00\">beta_education</text>\n",
       "<text text-anchor=\"middle\" x=\"1216\" y=\"-204.97\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
       "<text text-anchor=\"middle\" x=\"1216\" y=\"-185.47\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
       "</g>\n",
       "<!-- beta_education&#45;&gt;response_probs -->\n",
       "<g id=\"edge11\" class=\"edge\">\n",
       "<title>beta_education&#45;&gt;response_probs</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M1179.08,-165.81C1163.35,-150.74 1143.82,-135.18 1123,-126 1059.9,-98.17 866.99,-85.51 761.22,-80.52\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"761.68,-77.04 751.53,-80.08 761.35,-84.03 761.68,-77.04\"/>\n",
       "</g>\n",
       "<!-- beta_action -->\n",
       "<g id=\"node12\" class=\"node\">\n",
       "<title>beta_action</title>\n",
       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1384\" cy=\"-208.52\" rx=\"66.47\" ry=\"47.02\"/>\n",
       "<text text-anchor=\"middle\" x=\"1384\" y=\"-224.47\" font-family=\"Times,serif\" font-size=\"14.00\">beta_action</text>\n",
       "<text text-anchor=\"middle\" x=\"1384\" y=\"-204.97\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
       "<text text-anchor=\"middle\" x=\"1384\" y=\"-185.47\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
       "</g>\n",
       "<!-- beta_action&#45;&gt;response_probs -->\n",
       "<g id=\"edge13\" class=\"edge\">\n",
       "<title>beta_action&#45;&gt;response_probs</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M1355.81,-165.53C1343.34,-150.42 1327.33,-134.91 1309,-126 1214.2,-79.92 902.96,-76.09 761.33,-76.84\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"761.69,-73.34 751.71,-76.91 761.74,-80.34 761.69,-73.34\"/>\n",
       "</g>\n",
       "<!-- delta -->\n",
       "<g id=\"node13\" class=\"node\">\n",
       "<title>delta</title>\n",
       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1536\" cy=\"-354.07\" rx=\"53.21\" ry=\"47.02\"/>\n",
       "<text text-anchor=\"middle\" x=\"1536\" y=\"-370.02\" font-family=\"Times,serif\" font-size=\"14.00\">delta</text>\n",
       "<text text-anchor=\"middle\" x=\"1536\" y=\"-350.52\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
       "<text text-anchor=\"middle\" x=\"1536\" y=\"-331.02\" font-family=\"Times,serif\" font-size=\"14.00\">Dirichlet</text>\n",
       "</g>\n",
       "<!-- delta_education -->\n",
       "<g id=\"node14\" class=\"node\">\n",
       "<title>delta_education</title>\n",
       "<polygon fill=\"none\" stroke=\"black\" points=\"1597.62,-241.77 1474.38,-241.77 1474.38,-175.27 1597.62,-175.27 1597.62,-241.77\"/>\n",
       "<text text-anchor=\"middle\" x=\"1536\" y=\"-224.47\" font-family=\"Times,serif\" font-size=\"14.00\">delta_education</text>\n",
       "<text text-anchor=\"middle\" x=\"1536\" y=\"-204.97\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
       "<text text-anchor=\"middle\" x=\"1536\" y=\"-185.47\" font-family=\"Times,serif\" font-size=\"14.00\">Deterministic</text>\n",
       "</g>\n",
       "<!-- delta&#45;&gt;delta_education -->\n",
       "<g id=\"edge1\" class=\"edge\">\n",
       "<title>delta&#45;&gt;delta_education</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M1536,-306.86C1536,-289.68 1536,-270.25 1536,-253.29\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"1539.5,-253.52 1536,-243.52 1532.5,-253.52 1539.5,-253.52\"/>\n",
       "</g>\n",
       "<!-- delta_education&#45;&gt;response_probs -->\n",
       "<g id=\"edge14\" class=\"edge\">\n",
       "<title>delta_education&#45;&gt;response_probs</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M1515.84,-175.13C1502.84,-157.3 1484.22,-136.68 1462,-126 1399.86,-96.13 940.6,-83.13 761.36,-79.16\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"761.56,-75.66 751.48,-78.94 761.4,-82.66 761.56,-75.66\"/>\n",
       "</g>\n",
       "<!-- alpha -->\n",
       "<g id=\"node15\" class=\"node\">\n",
       "<title>alpha</title>\n",
       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1699\" cy=\"-208.52\" rx=\"47.91\" ry=\"47.02\"/>\n",
       "<text text-anchor=\"middle\" x=\"1699\" y=\"-224.47\" font-family=\"Times,serif\" font-size=\"14.00\">alpha</text>\n",
       "<text text-anchor=\"middle\" x=\"1699\" y=\"-204.97\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
       "<text text-anchor=\"middle\" x=\"1699\" y=\"-185.47\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
       "</g>\n",
       "<!-- alpha&#45;&gt;response_probs -->\n",
       "<g id=\"edge5\" class=\"edge\">\n",
       "<title>alpha&#45;&gt;response_probs</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M1678.51,-165.87C1668.76,-150.61 1655.56,-134.91 1639,-126 1562.49,-84.84 969.38,-78.75 761.61,-77.88\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"761.77,-74.38 751.76,-77.84 761.75,-81.38 761.77,-74.38\"/>\n",
       "</g>\n",
       "<!-- response_probs&#45;&gt;response -->\n",
       "<g id=\"edge15\" class=\"edge\">\n",
       "<title>response_probs&#45;&gt;response</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M629.78,-78.74C520.87,-81.38 296.03,-91.04 227,-126 212.31,-133.44 199.13,-145.1 188.11,-157.3\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"185.67,-154.77 181.81,-164.64 190.98,-159.32 185.67,-154.77\"/>\n",
       "</g>\n",
       "</g>\n",
       "</svg>\n"
      ],
      "text/plain": [
       "<graphviz.graphs.Digraph at 0x7f33ea49b1a0>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Add interaction of education level with gender to the dataset\n",
    "EDUCATION_LEVEL_ID, EDUCATION_LEVEL = pd.factorize(TROLLEY.education_level, sort=True)\n",
    "EDUCATION_LEVEL = [EDUCUATION_MAP_R[e] for e in EDUCATION_LEVEL]\n",
    "N_EDUCATION_LEVELS = len(EDUCATION_LEVEL)\n",
    "\n",
    "AGE = utils.standardize(TROLLEY.age.values)\n",
    "\n",
    "DIRICHLET_PRIOR_WEIGHT = 2.0\n",
    "\n",
    "coords = coords = {\n",
    "    \"GENDER\": GENDER,\n",
    "    \"EDUCATION_LEVEL\": EDUCATION_LEVEL,\n",
    "    \"CUTPOINTS\": np.arange(1, N_RESPONSE_CLASSES).astype(int),\n",
    "}\n",
    "\n",
    "with pm.Model(coords=coords) as direct_effect_education_model:\n",
    "\n",
    "    # Data for posterior predictions\n",
    "    action = pm.Data(\"action\", TROLLEY.action.astype(float))\n",
    "    intent = pm.Data(\"intent\", TROLLEY.intention.astype(float))\n",
    "    contact = pm.Data(\"contact\", TROLLEY.contact.astype(float))\n",
    "    gender = pm.Data(\"gender\", GENDER_ID)\n",
    "    age = pm.Data(\"age\", AGE)\n",
    "    education_level = pm.Data(\"education_level\", EDUCATION_LEVEL_ID)\n",
    "\n",
    "    # Priors (all gender-level parameters)\n",
    "    beta_action = pm.Normal(\"beta_action\", 0, 0.5, dims=\"GENDER\")\n",
    "    beta_intent = pm.Normal(\"beta_intent\", 0, 0.5, dims=\"GENDER\")\n",
    "    beta_contact = pm.Normal(\"beta_contact\", 0, 0.5, dims=\"GENDER\")\n",
    "    beta_education = pm.Normal(\"beta_education\", 0, 0.5, dims=\"GENDER\")\n",
    "    beta_age = pm.Normal(\"beta_age\", 0, 0.5, dims=\"GENDER\")\n",
    "\n",
    "    # Education deltas_{j=1...|E|}.\n",
    "    delta_dirichlet = pm.Dirichlet(\n",
    "        \"delta\", np.ones(N_EDUCATION_LEVELS - 1) * DIRICHLET_PRIOR_WEIGHT\n",
    "    )\n",
    "\n",
    "    # Insert delta_0 = 0.0 at index 0 of parameter list\n",
    "    delta_0 = [0.0]\n",
    "    delta_education = pm.Deterministic(\n",
    "        \"delta_education\", pm.math.concatenate([delta_0, delta_dirichlet]), dims=\"EDUCATION_LEVEL\"\n",
    "    )\n",
    "\n",
    "    # For each level of education, the cumulative delta_E, phi_i\n",
    "    cumulative_delta_education = delta_education.cumsum()\n",
    "\n",
    "    # Response cut points\n",
    "    cutpoints = pm.Normal(\n",
    "        \"alpha\",\n",
    "        mu=0,\n",
    "        sigma=1,\n",
    "        transform=pm.distributions.transforms.univariate_ordered,\n",
    "        shape=N_RESPONSE_CLASSES - 1,\n",
    "        initval=np.arange(N_RESPONSE_CLASSES - 1)\n",
    "        - 2.5,  # use ordering (with coarse log-odds centering) for init\n",
    "        dims=\"CUTPOINTS\",\n",
    "    )\n",
    "\n",
    "    # Likelihood\n",
    "    phi = (\n",
    "        beta_education[gender] * cumulative_delta_education[education_level]\n",
    "        + beta_age[gender] * age\n",
    "        + beta_action[gender] * action\n",
    "        + beta_intent[gender] * intent\n",
    "        + beta_contact[gender] * contact\n",
    "    )\n",
    "\n",
    "    pm.OrderedLogistic(\"response\", cutpoints=cutpoints, eta=phi, observed=RESPONSE_ID)\n",
    "\n",
    "pm.model_to_graphviz(direct_effect_education_model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "6c509c04-5212-4625-bf7f-88cdcf65aaa2",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Initializing NUTS using jitter+adapt_diag...\n",
      "Multiprocess sampling (4 chains in 4 jobs)\n",
      "NUTS: [beta_action, beta_intent, beta_contact, beta_education, beta_age, delta, alpha]\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "470f8182cb354e85816f6d1ec6a0118c",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Sampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 145 seconds.\n"
     ]
    }
   ],
   "source": [
    "with direct_effect_education_model:\n",
    "    direct_effect_education_inference = pm.sample()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "de5118f5-f8a6-4ac2-86cd-70c5c49f0a16",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>sd</th>\n",
       "      <th>hdi_3%</th>\n",
       "      <th>hdi_97%</th>\n",
       "      <th>mcse_mean</th>\n",
       "      <th>mcse_sd</th>\n",
       "      <th>ess_bulk</th>\n",
       "      <th>ess_tail</th>\n",
       "      <th>r_hat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>alpha[1]</th>\n",
       "      <td>-2.892</td>\n",
       "      <td>0.103</td>\n",
       "      <td>-3.082</td>\n",
       "      <td>-2.692</td>\n",
       "      <td>0.003</td>\n",
       "      <td>0.002</td>\n",
       "      <td>1474.0</td>\n",
       "      <td>2003.0</td>\n",
       "      <td>1.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>alpha[2]</th>\n",
       "      <td>-2.208</td>\n",
       "      <td>0.101</td>\n",
       "      <td>-2.404</td>\n",
       "      <td>-2.023</td>\n",
       "      <td>0.003</td>\n",
       "      <td>0.002</td>\n",
       "      <td>1518.0</td>\n",
       "      <td>1899.0</td>\n",
       "      <td>1.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>alpha[3]</th>\n",
       "      <td>-1.620</td>\n",
       "      <td>0.100</td>\n",
       "      <td>-1.817</td>\n",
       "      <td>-1.439</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>1648.0</td>\n",
       "      <td>1872.0</td>\n",
       "      <td>1.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>alpha[4]</th>\n",
       "      <td>-0.579</td>\n",
       "      <td>0.098</td>\n",
       "      <td>-0.775</td>\n",
       "      <td>-0.402</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>1634.0</td>\n",
       "      <td>2087.0</td>\n",
       "      <td>1.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>alpha[5]</th>\n",
       "      <td>0.108</td>\n",
       "      <td>0.098</td>\n",
       "      <td>-0.078</td>\n",
       "      <td>0.294</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>1628.0</td>\n",
       "      <td>2018.0</td>\n",
       "      <td>1.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>alpha[6]</th>\n",
       "      <td>1.037</td>\n",
       "      <td>0.100</td>\n",
       "      <td>0.833</td>\n",
       "      <td>1.212</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>1657.0</td>\n",
       "      <td>2151.0</td>\n",
       "      <td>1.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>beta_action[F]</th>\n",
       "      <td>-0.559</td>\n",
       "      <td>0.059</td>\n",
       "      <td>-0.673</td>\n",
       "      <td>-0.454</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.001</td>\n",
       "      <td>3411.0</td>\n",
       "      <td>2829.0</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>beta_action[M]</th>\n",
       "      <td>-0.807</td>\n",
       "      <td>0.054</td>\n",
       "      <td>-0.905</td>\n",
       "      <td>-0.701</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.001</td>\n",
       "      <td>3840.0</td>\n",
       "      <td>3128.0</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>beta_intent[F]</th>\n",
       "      <td>-0.662</td>\n",
       "      <td>0.052</td>\n",
       "      <td>-0.757</td>\n",
       "      <td>-0.567</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.001</td>\n",
       "      <td>3929.0</td>\n",
       "      <td>3222.0</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>beta_intent[M]</th>\n",
       "      <td>-0.756</td>\n",
       "      <td>0.049</td>\n",
       "      <td>-0.849</td>\n",
       "      <td>-0.663</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.001</td>\n",
       "      <td>3760.0</td>\n",
       "      <td>2364.0</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>beta_contact[F]</th>\n",
       "      <td>-0.768</td>\n",
       "      <td>0.072</td>\n",
       "      <td>-0.906</td>\n",
       "      <td>-0.636</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.001</td>\n",
       "      <td>3835.0</td>\n",
       "      <td>3171.0</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>beta_contact[M]</th>\n",
       "      <td>-1.092</td>\n",
       "      <td>0.067</td>\n",
       "      <td>-1.218</td>\n",
       "      <td>-0.962</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.001</td>\n",
       "      <td>3807.0</td>\n",
       "      <td>2790.0</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>beta_education[F]</th>\n",
       "      <td>-0.629</td>\n",
       "      <td>0.132</td>\n",
       "      <td>-0.889</td>\n",
       "      <td>-0.383</td>\n",
       "      <td>0.003</td>\n",
       "      <td>0.002</td>\n",
       "      <td>1751.0</td>\n",
       "      <td>2265.0</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>beta_education[M]</th>\n",
       "      <td>0.407</td>\n",
       "      <td>0.132</td>\n",
       "      <td>0.169</td>\n",
       "      <td>0.660</td>\n",
       "      <td>0.003</td>\n",
       "      <td>0.002</td>\n",
       "      <td>1797.0</td>\n",
       "      <td>2180.0</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>beta_age[F]</th>\n",
       "      <td>-0.001</td>\n",
       "      <td>0.028</td>\n",
       "      <td>-0.049</td>\n",
       "      <td>0.054</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>3850.0</td>\n",
       "      <td>2987.0</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>beta_age[M]</th>\n",
       "      <td>-0.133</td>\n",
       "      <td>0.028</td>\n",
       "      <td>-0.185</td>\n",
       "      <td>-0.079</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.000</td>\n",
       "      <td>2720.0</td>\n",
       "      <td>2807.0</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>delta_education[Elementary School]</th>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>4000.0</td>\n",
       "      <td>4000.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>delta_education[Middle School]</th>\n",
       "      <td>0.152</td>\n",
       "      <td>0.089</td>\n",
       "      <td>0.009</td>\n",
       "      <td>0.309</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.001</td>\n",
       "      <td>3154.0</td>\n",
       "      <td>1911.0</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>delta_education[Some High School]</th>\n",
       "      <td>0.151</td>\n",
       "      <td>0.085</td>\n",
       "      <td>0.014</td>\n",
       "      <td>0.304</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.001</td>\n",
       "      <td>3338.0</td>\n",
       "      <td>2363.0</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>delta_education[High School Graduate]</th>\n",
       "      <td>0.284</td>\n",
       "      <td>0.110</td>\n",
       "      <td>0.088</td>\n",
       "      <td>0.489</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.001</td>\n",
       "      <td>3164.0</td>\n",
       "      <td>1833.0</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>delta_education[Some College]</th>\n",
       "      <td>0.080</td>\n",
       "      <td>0.051</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.172</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.001</td>\n",
       "      <td>3342.0</td>\n",
       "      <td>2077.0</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>delta_education[Bachelor's Degree]</th>\n",
       "      <td>0.058</td>\n",
       "      <td>0.044</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.137</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.001</td>\n",
       "      <td>2334.0</td>\n",
       "      <td>2120.0</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>delta_education[Master's Degree]</th>\n",
       "      <td>0.237</td>\n",
       "      <td>0.070</td>\n",
       "      <td>0.104</td>\n",
       "      <td>0.365</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.001</td>\n",
       "      <td>3256.0</td>\n",
       "      <td>2183.0</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>delta_education[Graduate Degree]</th>\n",
       "      <td>0.037</td>\n",
       "      <td>0.025</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.082</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>5157.0</td>\n",
       "      <td>3405.0</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                        mean     sd  hdi_3%  hdi_97%  \\\n",
       "alpha[1]                              -2.892  0.103  -3.082   -2.692   \n",
       "alpha[2]                              -2.208  0.101  -2.404   -2.023   \n",
       "alpha[3]                              -1.620  0.100  -1.817   -1.439   \n",
       "alpha[4]                              -0.579  0.098  -0.775   -0.402   \n",
       "alpha[5]                               0.108  0.098  -0.078    0.294   \n",
       "alpha[6]                               1.037  0.100   0.833    1.212   \n",
       "beta_action[F]                        -0.559  0.059  -0.673   -0.454   \n",
       "beta_action[M]                        -0.807  0.054  -0.905   -0.701   \n",
       "beta_intent[F]                        -0.662  0.052  -0.757   -0.567   \n",
       "beta_intent[M]                        -0.756  0.049  -0.849   -0.663   \n",
       "beta_contact[F]                       -0.768  0.072  -0.906   -0.636   \n",
       "beta_contact[M]                       -1.092  0.067  -1.218   -0.962   \n",
       "beta_education[F]                     -0.629  0.132  -0.889   -0.383   \n",
       "beta_education[M]                      0.407  0.132   0.169    0.660   \n",
       "beta_age[F]                           -0.001  0.028  -0.049    0.054   \n",
       "beta_age[M]                           -0.133  0.028  -0.185   -0.079   \n",
       "delta_education[Elementary School]     0.000  0.000   0.000    0.000   \n",
       "delta_education[Middle School]         0.152  0.089   0.009    0.309   \n",
       "delta_education[Some High School]      0.151  0.085   0.014    0.304   \n",
       "delta_education[High School Graduate]  0.284  0.110   0.088    0.489   \n",
       "delta_education[Some College]          0.080  0.051   0.001    0.172   \n",
       "delta_education[Bachelor's Degree]     0.058  0.044   0.002    0.137   \n",
       "delta_education[Master's Degree]       0.237  0.070   0.104    0.365   \n",
       "delta_education[Graduate Degree]       0.037  0.025   0.001    0.082   \n",
       "\n",
       "                                       mcse_mean  mcse_sd  ess_bulk  ess_tail  \\\n",
       "alpha[1]                                   0.003    0.002    1474.0    2003.0   \n",
       "alpha[2]                                   0.003    0.002    1518.0    1899.0   \n",
       "alpha[3]                                   0.002    0.002    1648.0    1872.0   \n",
       "alpha[4]                                   0.002    0.002    1634.0    2087.0   \n",
       "alpha[5]                                   0.002    0.002    1628.0    2018.0   \n",
       "alpha[6]                                   0.002    0.002    1657.0    2151.0   \n",
       "beta_action[F]                             0.001    0.001    3411.0    2829.0   \n",
       "beta_action[M]                             0.001    0.001    3840.0    3128.0   \n",
       "beta_intent[F]                             0.001    0.001    3929.0    3222.0   \n",
       "beta_intent[M]                             0.001    0.001    3760.0    2364.0   \n",
       "beta_contact[F]                            0.001    0.001    3835.0    3171.0   \n",
       "beta_contact[M]                            0.001    0.001    3807.0    2790.0   \n",
       "beta_education[F]                          0.003    0.002    1751.0    2265.0   \n",
       "beta_education[M]                          0.003    0.002    1797.0    2180.0   \n",
       "beta_age[F]                                0.000    0.000    3850.0    2987.0   \n",
       "beta_age[M]                                0.001    0.000    2720.0    2807.0   \n",
       "delta_education[Elementary School]         0.000    0.000    4000.0    4000.0   \n",
       "delta_education[Middle School]             0.001    0.001    3154.0    1911.0   \n",
       "delta_education[Some High School]          0.001    0.001    3338.0    2363.0   \n",
       "delta_education[High School Graduate]      0.002    0.001    3164.0    1833.0   \n",
       "delta_education[Some College]              0.001    0.001    3342.0    2077.0   \n",
       "delta_education[Bachelor's Degree]         0.001    0.001    2334.0    2120.0   \n",
       "delta_education[Master's Degree]           0.001    0.001    3256.0    2183.0   \n",
       "delta_education[Graduate Degree]           0.000    0.000    5157.0    3405.0   \n",
       "\n",
       "                                       r_hat  \n",
       "alpha[1]                                1.01  \n",
       "alpha[2]                                1.01  \n",
       "alpha[3]                                1.01  \n",
       "alpha[4]                                1.01  \n",
       "alpha[5]                                1.01  \n",
       "alpha[6]                                1.01  \n",
       "beta_action[F]                          1.00  \n",
       "beta_action[M]                          1.00  \n",
       "beta_intent[F]                          1.00  \n",
       "beta_intent[M]                          1.00  \n",
       "beta_contact[F]                         1.00  \n",
       "beta_contact[M]                         1.00  \n",
       "beta_education[F]                       1.00  \n",
       "beta_education[M]                       1.00  \n",
       "beta_age[F]                             1.00  \n",
       "beta_age[M]                             1.00  \n",
       "delta_education[Elementary School]       NaN  \n",
       "delta_education[Middle School]          1.00  \n",
       "delta_education[Some High School]       1.00  \n",
       "delta_education[High School Graduate]   1.00  \n",
       "delta_education[Some College]           1.00  \n",
       "delta_education[Bachelor's Degree]      1.00  \n",
       "delta_education[Master's Degree]        1.00  \n",
       "delta_education[Graduate Degree]        1.00  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "az.summary(\n",
    "    direct_effect_education_inference,\n",
    "    var_names=[\n",
    "        \"alpha\",\n",
    "        \"beta_action\",\n",
    "        \"beta_intent\",\n",
    "        \"beta_contact\",\n",
    "        \"beta_education\",\n",
    "        \"beta_age\",\n",
    "        \"delta_education\",\n",
    "    ],\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "366f76f5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Verify that education deltas are on the 8-simplex\n",
    "assert np.isclose(\n",
    "    az.summary(direct_effect_education_inference, var_names=[\"delta_education\"])[\"mean\"].sum(),\n",
    "    1.0,\n",
    "    atol=0.01,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "87fe2fd9",
   "metadata": {},
   "source": [
    "### Running Counterfactuals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "791348e2",
   "metadata": {},
   "outputs": [],
   "source": [
    "def run_direct_education_counterfactual(\n",
    "    action=0, intent=0, contact=0, gender=0, education_level=4, ax=None\n",
    "):\n",
    "    with direct_effect_education_model:\n",
    "        pm.set_data(\n",
    "            {\n",
    "                \"action\": xr.DataArray([action] * N_TROLLEY_RESPONSES),\n",
    "                \"intent\": xr.DataArray([intent] * N_TROLLEY_RESPONSES),\n",
    "                \"contact\": xr.DataArray([contact] * N_TROLLEY_RESPONSES),\n",
    "                \"gender\": xr.DataArray([gender] * N_TROLLEY_RESPONSES),\n",
    "                \"education_level\": xr.DataArray([education_level] * N_TROLLEY_RESPONSES),\n",
    "            }\n",
    "        )\n",
    "        ppd = pm.sample_posterior_predictive(\n",
    "            direct_effect_education_inference, extend_inferencedata=False\n",
    "        )\n",
    "\n",
    "    # recode back to 1-7\n",
    "    posterior_predictive = ppd.posterior_predictive[\"response\"] + 1\n",
    "\n",
    "    if ax is not None:\n",
    "        plt.sca(ax)\n",
    "    else:\n",
    "        plt.subplots(figsize=(6, 3))\n",
    "\n",
    "    counts, bins = np.histogram(posterior_predictive.values.ravel(), bins=np.arange(1, 9))\n",
    "    total_counts = counts.sum()\n",
    "    counts = [c / total_counts for c in counts]\n",
    "    plt.bar(bins[:-1], counts, width=0.25)\n",
    "\n",
    "    plt.xlim([0.5, 7.5])\n",
    "    plt.xlabel(\"response\")\n",
    "    plt.ylabel(\"density\")\n",
    "    plt.title(\n",
    "        f\"action={action}; intent={intent}; contact={contact}\\ngender={gender}; education_level={education_level}\"\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "09265676-4aed-4bf6-8ffe-9b9f8e619db8",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Sampling: [response]\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "9b89b6ea09fa41d398219d3ce3288c52",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 600x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "run_direct_education_counterfactual(education_level=4, gender=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "81dc67c4",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Sampling: [response]\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "cd18073ec9344b8999d35d177b8dbd52",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAmMAAAE3CAYAAADmP0YBAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjkuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8hTgPZAAAACXBIWXMAAA9hAAAPYQGoP6dpAABTCklEQVR4nO3dd1QU19sH8O9SLLh0xYIIioIFEHtDTdTYUGOvPzvWgL2XmBijxsQKsSYae0GwxIpobFiwY0ERUURQRKoIygL7/uG7Gza7lF0XZtHv5xzPcWfu3Hn2zs7uw507d0RSqVQKIiIiIhKEntABEBEREX3JmIwRERERCYjJGBEREZGAmIwRERERCYjJGBEREZGAmIwRERERCYjJGBEREZGAmIwRERERCYjJGBEREZGAmIwR5eHq1atwdHTE4MGDhQ6lWHN0dISjo6PQYRAR6SQDoQMgEpK3tzcAwMvLS+BIit6tW7ewceNG3Lp1C2lpaahcuTLc3d3h4eGBkiVLCh1envz9/REdHY0ePXqgcuXKQoeD0NBQBAYGolatWmjXrl2R7ffQoUPYvXs3Hj9+DKlUCgcHBwwcOBDdunUrshh0gVDtr4oQn82UlBSsW7cOp06dQmxsLExNTdGkSROMHz8e9vb2RRIDfRr2jNEXzcfHBz4+PrmuL126NKpWrYqKFSsWYVSF7/Dhwxg0aBDOnDmDEiVKwN7eHs+fP8eaNWswaNAgpKena3V/VatWRdWqVbVW34EDB+Dj44Po6Git1fkpQkND4ePjg8DAwCLb5/fff48ZM2bg1q1bsLKyQoUKFXDr1i1Mnz4dCxcuLLI4dIEQ7Z+bov5sxsXFoXv37ti8eTPevHmDGjVqICsrC0eOHEHPnj1x7dq1IomDPg2TMaI8uLi44MSJE1i2bJnQoWjNixcvMHfuXGRlZWH69Ok4d+4cDhw4gJMnT6Jq1aq4e/cufv31V63u88SJEzhx4oRW6/ySHT16FHv37oWRkRH++usvHD9+HMeOHcNff/0FIyMj7Ny5k+39hZg1axaio6PRoEED/PPPP/D398f58+cxePBgvH//HpMmTUJaWprQYVI+mIwRfWH+/PNPZGRkwM3NDR4eHhCJRAAAa2trLF68GACwb98+vHnzRsgwKQ/r1q0DAIwdOxbNmjWTL2/WrBnGjBkDAFi7dq0gsVHRuXv3Li5evAgDAwP89ttvMDc3BwAYGhpi9uzZsLe3x5s3b7Bv3z6BI6X8iKRSqVToIIj+KywsDCdOnEBQUBCio6ORlJQEMzMzuLq6YsSIEahfv36u28bGxmLz5s24cOECYmJioK+vj0qVKsHNzQ39+vWDnZ0dvL2987w8efr0aVSuXBlXr17FkCFD0LhxY2zfvl2pXExMDDZs2IALFy7g9evXKFOmDJydnTF48GC0bt1aqbxsv56enhg2bBjWrFmDU6dO4c2bN6hYsSK6d++OMWPGwMCgcIZzSqVStGzZEnFxcVi5ciU6d+6sVKZTp06IiIjAwoUL0a9fP63sVzZ4/9GjRwrL27Rpg+joaJw+fRpv3ryBj48P7ty5A4lEgtq1a8PLy0sh2ZAdj9wsWbIEPXv2lL9OT0/H9u3bceLECTx79gxZWVmws7ND165dMWTIEJQoUUJhe02Oj+w9qJLb5+ZTREREoFOnTgCAoKAglC1bVmF9XFwc3NzcAHzskdTm5eGCnFs5FcX5UdD2j4uLw4kTJ3D27Fk8ffoUcXFxKFWqFBwdHdG7d29079491/ednp6OXbt24eTJk4iIiIBEIkH58uVRt25d9OnTB40bN1b7s6kNy5cvx8aNG9GqVSts2rRJaf3GjRuxfPly1KtXD3v27NHqvkm7OICfdNLixYtx+fJlmJiYoFy5crCyskJMTAxOnTqFM2fO4JdffkHXrl2Vtrt8+TI8PT2RmpoKQ0NDVKtWDVKpFFFRUdi8eTOMjIzg5eWFihUron79+rh58yYAKCV3BRnAfufOHXh4eCAlJQVGRkZwcHBAfHw8Lly4gAsXLmD8+PGYOHGiym3fvn2Lfv36ITIyEjVq1ICenp58zNbLly+xaNEiDVotfzExMYiLiwMANGjQQGWZ+vXrIyIiAnfu3FFIxl69eoW+ffsC+NhzVqFCBa3FdfbsWSxduhRlypSBjY0Nnj9/jhs3bsDDwwObN29GkyZNAADGxsaoX78+wsLCkJqaCgcHB4jFYnk9lpaW8v/HxsZixIgRCA8Ph4GBAaytrWFgYIDw8HD8+uuvOHPmDDZv3oxSpUopxaPO8XFycoKhoSGePXsGS0tL2Nraytc5ODhorY1k7ty5AwCwtbVVSsQAoFy5cqhSpQqeP3+OO3fuKCRjx48fx5IlS1CxYkXs3btXrf0W9NzKGWdRnB8FbX9fX1+sXr0apUqVgpWVFRwcHJCQkIBr167h2rVruHXrFn788UelWGJiYuDh4YEnT54AAOzs7FCmTBlER0fj8OHDePXqFbZv367WZ1Nbbt++DUD5+0tGtvzevXvIysqCvr6+1mMg7WAyRjqpf//+mD17tsJ0CFKpFKdPn8b06dPxww8/4Ouvv1b4souJiYGXlxdSU1PRvXt3zJ49G2ZmZgCA7OxsnD9/HtnZ2QCA3r17o3fv3vL6d+/erVZ86enpmDRpElJSUtCpUycsWrRIHsuBAwcwd+5crF27Fq6urip7AHbt2gVXV1ds2bIF5cuXBwCcOXMGnp6e8PX1xfDhwxXugoqLi8OECRPUihEAateujfnz58tfR0ZGAgBKlCgBKysrldvY2NgolJXJzMxEbGys/P/atHTpUkyYMAEjR46Evr4+JBIJZs+ejb///hvLly+XX2apXbs2du/ejcGDByM4OBjz5s2TJ2o5ZWdnY+LEiQgPD4e7uzvmzJkjT1xevXqFqVOn4vr161i9ejVmzpyptL06x2fNmjXw9/fH7Nmz0apVKyxdulTlezx37hzWr1+vdtv06tULvXv3lr9+9uwZgH+PkyqyhPa/xzA9PR2xsbFq97yqc27J9lNU50dB279x48bYunUrGjVqpJCUPHz4EFOmTMGePXvg7u6Oxo0by9dlZWXB09MTT548gZOTE5YtW6ZwXoaGhuLWrVsACv7ZBICffvoJDx48yLfd/2vNmjUoV66c/LXs+Ob2WZAtl0gkiImJyfMzQ8JiMkY6qWPHjkrLRCIR2rVrh6FDh2LdunX4559/FHrHNm3ahLdv36JZs2ZYunSpfCwUAOjp6eGrr77SWnxHjhxBTEwMypYti19++UWhJ61Hjx4ICQnBrl27sHHjRpU/Nvr6+vjtt9/kPzTAx8stbdu2RUBAAC5cuKDwpf/hwwd5L546/vujm5ycDAAwMTFRaJ+cTExMAHy8Xb6ouLm5YfTo0fLXhoaGmDNnDk6ePIk7d+4gOTkZpqamBa7v7NmzuHXrFpydnbFs2TKFdqhQoQJWrlyJDh06YM+ePZg4caJS75i6x6cg4uPjNTqGzZs3V3gtOy6y46SKto+huudWUZ8fBdGwYUOVy2vWrIn58+dj2LBh+PvvvxWSsYCAANy/fx+Wlpb4448/5GOyZGrVqoVatWqpFQfwcRiGJp+FDx8+KLzO77OQ85xJTk5mMqbDmIyRzoqJicGRI0dw//59JCYmQiKRAAASEhIAfPyLNmcydvr0aQDAyJEjc000tOXixYsAgD59+qi8pDlkyBDs2rVLPoeXkZGRwvqWLVuqvMzn7OyMgIAAREVFKSyvXLmy0ngrTci+zA0NDXMtIxtH9f79+0KJQZU+ffooLbOwsIC1tTWePn2KqKgotZKxU6dOAfj4w6+qF8jKygrOzs64evUq7t27p/RDre7xKYiePXtqZczQpxxDTWNQ99wq6vOjoFJTU3Hs2DHcuHEDcXFxeP/+PaRSKTIyMgB8/E7JSfa+e/XqpZSIfQptjSPM77OQc0zkfz8LpFuYjJFOOnDgABYsWKD0l2BOsl4e4OOXrOwSmqura2GHJ79UVL16dZXr7ezsYGhoCIlEgufPn6NmzZoK63P7C9XCwgIACu1WdNkPoyyxVUX2w6RqLFVhqVKlisrllpaWePr0qdrtIUsa9+zZgyNHjqgsIzuGr1+/Vlon1PEpiKI+hpqcW7p4fjx48ABjxoxRebxlcn6nAB9vlgCAunXrqr2/olCyZEmkp6fn+lmQfQ6Aoj2fSX1MxkjnPH/+HPPnz4dEIsGIESPQrVs32NjYoEyZMhCJRPD19cW8efMUxi29e/dO/n9jY+NCj1H2Y5DboFyRSAQLCwvExsYqxCbz354AGT29j7PNFNZNzrLepZSUFEilUpW9HAW5DKZtpUuXVrlc0/ZITU0F8PFyUH5U9RgIdXwKoiCXILV5DDU5t3Tt/MjKysKkSZPw+vVrtG7dGqNGjUL16tVhYmICfX19REZGon379kpjIWWfo6I8F9RhYmKC9PT0XD8LOZNLdXqWqegxGSOdc/z4cUgkEri7u6scXP3y5UulZWXKlJH//+3bt4WekMl+LOLj41Wul0ql8supOWPTlLYG8MvuNMvIyMDr168VxuTIyC4B5bwrrbiRHZ8tW7YojbkSirYG8Mumj8jrUp02j6Em51ZRnx/5CQkJQWRkJKytreHj46M0pYmq75ScsWl7/KS2BvDb2toiNjY218+CbLmhoSEqVaqkWbBUJJiMkc6RzRlUr149lev/O64DAMRiMSpUqIBXr17h9u3baNmyZaHGaGdnh4cPHyI8PFzl+mfPnkEikUBfXz/XS3Dq0NYA/kqVKqFcuXKIi4vDjRs3VM4zJtuPrl6aKQh7e3uEhoYiLCysSJKxgoyj0tYAfhcXFwAf76R78+aNynnGnj9/DkA7x1CTc6uoz4/82l/2nVKnTh2lRAxQ/Z0CfLzM+uDBA9y5c0erz7zU1gD+unXrIjg4ONe6ZMudnJw4rYWOYzJGOkc2JkbVDPBPnjzBP//8o3K7du3aYceOHdiyZUuBk7FSpUrh/fv3eP/+vVpjKtzc3HDixAn4+vpi3LhxSoOUZQN069evn+slF3Voa/C87I7U3bt3w8/PTykZu3nzJiIiImBoaIg2bdp88v4Ki+xY5TYouX379jhy5Aj27t2LAQMGFPqDz2X15zVIWlsD+O3t7WFvb48nT57Az89PPuO+jJ+fH4CPc2xpa8JXdc+toj4/8mt/2edF1XeKRCLBtm3bVG7Xrl07HD58GH5+fhg5cqR8Oo+85PfZBLQ3gL99+/bYtGkTLl26hJiYGIXer6ysLBw8eBAA0KFDB63sjwoPH4dEOkc2Genu3bsRGhoqX/706VNMmjQp1zuHRo4cCWNjYwQFBWHOnDkK4yWys7Nx7tw5pURONlA4ODhYrRi7dOmCSpUq4c2bN5g1a5bCuJdDhw7JJ9QcNWqUWvUWhZEjR8LQ0BAXL17EH3/8IR9/Ex0djTlz5gD4OA9bzsshwMdnWjo6OsLR0REvXrwo8rhzkh233B6C/M0338DV1RUREREYO3as0nxbGRkZOHv2LGbPnq3VeO7evav1h6yrMm7cOADA+vXrcfnyZfnyy5cvY8OGDQplcvL394ejo6Paiba651ZRnx/5tX/dunVhYGCAmzdvyhMU4ONl12nTpuX66K927drByckJ8fHxGD16tHxAv8zDhw+xa9culbEUxQO6XVxc0KJFC2RmZmLatGlITEwE8DHBXLJkCZ48eQJLS0v5ZM2ku9gzRjqnXbt2cHV1xe3bt9GrVy/Y2dlBX18fjx8/RtmyZTFu3DisWrVKabtKlSphzZo18PT0hJ+fHw4fPgx7e3tkZ2fjxYsXSEtLg6enJ77++mv5Np06dcLjx48xduxYODo6yiemXLFihVIyklPp0qWxatUqjBw5EseOHcPZs2dRrVo1xMfHy8efjBs3TuUcSkKzsbHBokWLMHv2bPz666/Ytm0bLC0t8fjxY0gkEtSpUwczZswQOsw8de7cGTt37sSmTZtw6tQplCtXDiKRCKNGjUKrVq2gp6cHb29vjBkzBpcuXUL79u1ha2sLMzMzvHv3DpGRkZBIJCpnsNdEnTp1YGdnh2fPnuGrr75C1apVYWhoiJo1a2Lu3Lla2UdOXbt2RXBwMPbt24dhw4bJ59ySzRLfv39/lZegNaXuuVXU50d+7V+uXDkMGTIEmzdvxsyZM7F69WqYm5vjyZMnyMrKwty5c/HDDz8o1auvrw9vb2+MGDECd+7cQadOnRRm4E9KSkLjxo0xcOBA+Tb5fTa1bcmSJRgwYABu3LiBr7/+GtWqVcPLly+RkJCAkiVLYuXKlUUyLo8+DZMx0jkGBgb4888/sWrVKpw8eRLPnz+HpaUlevfujQkTJsjnMFKlefPmOHLkCP78809cuHABERERKFWqFGxsbODm5oZvv/1Wofzo0aORnZ2No0ePIjw8XH4reF5TasjUrVsXhw4dwoYNG3Dx4kU8evQIRkZGcHNzw5AhQ3QyEZPp3r07qlSpgo0bN+LWrVsIDw9H5cqV0aVLF4waNUrlZT1Z74GlpWWus/cXlYYNG2L58uXYunUrwsPD5VMp9OjRQ17GysoKe/fuxf79+3Hs2DGEhYXJJyJ1cXFB8+bN5c94/FR6enrYsGEDVqxYgevXryMkJARZWVlaqTs3P/30Exo0aIDdu3fL7xp1dXXFgAEDcn3OouwY/ncqiYJQ99wqyvOjIO0/Y8YMVKhQAXv27EFUVBTS09PRrFkzjBs3Ls9HFVWqVAn+/v7YsWMHTpw4gadPn0IqlaJ8+fL4+uuvFW6uAAr22dSm8uXL48CBA1i7di0CAwMRFhYGExMTuLu7Y/z48blOL0K6hQ8KJ6IC2bJlC5YuXQovLy94enoKHQ5pYOzYsfjnn3+wY8cONGrUSOhwiOj/ccwYERXIzZs3UapUKYVLMlS83L59G05OTkzEiHQMkzEiKpBbt27h22+/lc+CTsVLREQEEhMTMXz4cKFDIaL/4GVKIiIiIgGxZ4yIiIhIQEzGiIiIiATEZIyIiIhIQEzGiIiIiATEZIyIiIhIQEzGiIqY7BmPuvwg7i/B53wcZM+gnDVrltChFJi3tzccHR3h7e0tdChqa9OmjU48s5WKLz4OiYjU8v79e1y4cAEhISEICQnBvXv3kJqaiipVquDUqVNCh/fZS0lJwdatW2FsbIxhw4YJHQ4JaNq0afj7778BAMuWLVN6JBUVH0zGiEgtT58+5eOQBJSSkgIfHx9YW1vnmowZGxujatWqeT7snoq3S5cuyRMxKv6YjBGRWgwMDODq6gpnZ2c4OzsjMzMTc+bMETosyuGbb77BN998I3QYVEg+fPiAH374AVZWVihXrhzu378vdEj0iZiMEZFaatSogb1798pfX716VcBoiL48a9euRWRkJFauXIndu3cLHQ5pAQfwU7Hz/PlzTJkyBU2bNkXdunXx7bffyr+Q8hpIK5VKcfToUQwfPhxNmjSBk5MT2rZti0WLFiEuLk6p/NWrV+Ho6IjBgwcjOzsbW7duRZcuXeDs7IzmzZtjzpw5SEhIyDXO4OBgDBs2DPXr10eDBg0wePBgBAUF5fv+0tPTsXHjRvTs2RP169eXv8c//vgDGRkZSuVzDnxOSEjAwoUL0aZNG9SpU6dYDeDOj7rHT0bd45DzuKuS38D/9PR0/Pnnn+jbty8aNmyIunXron379pg+fTqCg4MVykZFRWHjxo0YPHgwWrduDScnJzRt2hQjR47E2bNnleqeNWsW2rZtCwCIjo6Go6Ojwj+Z/AbwP378GNOnT0erVq3g5OSE5s2bw8vLC7dv31ZZftasWXB0dIS/vz9iY2Mxe/ZsuLm5wdnZGe7u7ti5c6fK7bTp1atXWLRoETp06AAXFxc0bNgQgwcPxokTJxTKZWVloUWLFnB0dMTdu3dzrW/p0qVwdHTEkiVLlNaFhIRg8uTJaNmypbx9JkyYgAcPHmj9fanryZMn+PPPP9GiRQt07txZ6HBIS9gzRsXKw4cPMXjwYKSkpKBUqVKwt7dHUlISfvjhBzx58iTX7SQSCaZNmyb/4rayskKFChUQGRmJ7du348SJE9i+fTuqVq2qcvvp06fjyJEjsLOzg62tLZ4+fQo/Pz+EhITA398fJUqUUCh/9OhRTJs2DdnZ2TAzM0PlypURFhYGDw8PTJ48Odc4Y2NjMWLECISHh8PAwADW1tYwMDBAeHg4fv31V5w5cwabN29GqVKllLZNSEhAr169EBsbi+rVq0MsFkNPT7f+3urXrx9evnyJ2bNno1OnTgXeTtPjp+lx0FRMTAw8PDzkn0U7OzuUKVMG0dHROHz4MF69eoXt27fLy69fvx779++HkZERrKys4OjoiNjYWFy8eBEXL17E1KlTMXr0aHl5Ozs7ODk54d69eyhRogScnJzUjvH06dOYNGkSMjIyYGJiAkdHR8TExCAgIACBgYH48ccf0bdv31zfX69evZCSkoLq1atDJBIhPDwcCxcuREpKCsaNG6d2PAURHByM8ePH4+3btyhVqhRsbW3x9u1bBAcHIzg4GCNGjMDMmTMBAPr6+ujUqRO2b9+Oo0ePwtnZWak+qVSKY8eOAQDc3d0V1v31119YunQppFIpzMzMUKNGDcTExODkyZM4c+YMVqxYgfbt2xfK+8yPVCrF999/D5FIhO+//16QGKhwMBmjYiM7OxvTp09HSkoKWrVqhd9++w2mpqYAgJMnT2LatGnI7bn3a9aswYkTJ1C7dm0sXrwYtWrVAvDxzsBffvkFu3btwrRp0+Dn56e07a1bt2Bubg5fX1+4uLgA+DiIfdiwYXj8+DH8/PwwYMAAefnY2FjMmzcP2dnZGD16NCZOnAgDAwNIJBL89ttvWLNmTa7vb+LEiQgPD4e7uzvmzJmDsmXLAvjYKzB16lRcv34dq1evlv/w5LR37164uLhg9+7dqFChAoCPY0tkJkyYkGcPUm60eRkkLi4OsbGxSE9PV2s7TY6fpsdBU1lZWfD09MSTJ0/g5OSEZcuWwd7eXr4+NDQUt27dUtimffv26Nu3L1xcXCASieTLr1+/jkmTJmHVqlXo2LEjqlSpAgAYO3YsunTpgrZt26JcuXJqH5vY2FjMmDEDGRkZGDJkCKZPn44SJUogOzsbGzduxMqVK7Fw4UK4uLigZs2aStuvX78ebdq0waJFi2BiYgIA2LVrF3788UesW7cOgwYNki/XltjYWHh5eSE1NRVTpkzB8OHD5X/83Lx5E5MmTcLmzZvRuHFjfP311wCALl26YPv27Th27BhmzJih9EfJtWvXEBsbC1tbW/k5DQDnz5/H0qVLYWZmhoULFyokXb6+vvjhhx8wa9YsuLq6wsrKKt/Yz507h/Xr16v9nnv16oXevXsrLd+/fz+uX7+OcePGwc7OTu16SXfp1p/NRHkICgpCWFgYzMzMsGLFCnkiBgAdOnTA6NGjIZFIlLZLSEjAX3/9BbFYjHXr1sl/yAGgVKlSmD9/PpydnXHv3j1cv35daXuJRIL58+crfGlXrVoVHh4eAD5+gee0e/dupKWlwdnZGVOnToWBwce/eQwNDTF79mxUq1ZN5fs7e/Ysbt26BWdnZyxbtkyeiAFAhQoVsHLlShgZGWHPnj14//690vb6+vpYvXq1PBEDgJIlS8r/f+/ePdy8eVPtf0LT9Phpehw0FRAQgPv378PS0hJ//PGHQiIGALVq1cLAgQMVlrVu3Rp169ZVSMQAoGHDhpg4cSKysrLkPTjasGvXLqSmpqJWrVqYO3euPKnR09PD2LFj0bp1a0gkEmzevFnl9mZmZli6dKlCwjVw4EDUqVMHHz58KJTxg1u2bEFSUhKGDh2KMWPGKPRC169fHz/++COAjz1aMq6urrCxsUFsbKzKc/rIkSMAlHvFVq1aBalUip9//lmp96tPnz4YMmQI3r17B19f3wLFHh8fr9E59/LlS6W6EhIS8Ntvv6Fy5coYO3ZsgfZPxQd7xqjYuHTpEoCPvQnGxsZK63v27AkfHx+l5efOnUNGRgbatGmjkKjI6Onp4auvvsLdu3cRHByMhg0bKqw3NTVVeVlCdvnjv+PTLl68CAAKvWU5DRw4EAsWLFBaLpujq0ePHvLEIScrKys4Ozvj6tWruHfvnlKczZs3R/ny5VXuEwDOnDmT67qiokkMmh4/TY+Dpk6fPg3gY6+Gubl5gbdLSEjA33//jZCQEMTHx8t7M1NTUwF8vDSvLbKxcoMGDVK5fsiQITh37lyuY+rc3d1hZGSktNzJyQn3799HVFSU1mKVCQgIAPAxGVKlZcuWMDQ0xK1bt5CZmSk/d9zd3bF+/XocOXIEjRs3lpfPzMzEyZMnAXzsQZOJjo6WJ9OycXn/1aZNG2zevBnXrl0rUOw9e/ZEz549C1Q2P4sXL0ZSUhJ++eUXlcMUqHhjMkbFxrNnzwBAYaByTtbW1hCLxfIfMZmwsDAAwJ07d3L9YY6Pjwfw8ZLIf9nY2KjcxtLSEgDw7t07lXH+t2dEJrfljx49AgDs2bNH/pf7f8nqfv36tdI6bff06ApNj5+mx0FTERERAIC6desWeJuLFy9i0qRJePv2ba5lkpOTPzk2GVmbVK9eXeX6GjVqAADevHmD1NRUiMVihfWyy6X/JTsX0tLStBTpR+/evUN0dDQAYP78+XmW/fDhA5KSkuQ9yl27dsX69etx8uRJzJ8/H4aGhgA+JqRJSUmoVauWwmdA9jn78OFDrp8zWaKs6nuiMF2+fBl///032rZti6+++qpI901Fg8kYFRuycUZlypTJtUyZMmWUkjHZD93Lly9Vdv/nlHOMlYyqngAASpeWZGQ/SBYWFirXy364/ksWt+xHIS+qLlPmFmdxp+nx0/Q4aEp2/Ao6ZiolJQVTpkzB27dv0b17dwwcOBBVq1aV33hx6dIlDB8+HJmZmVqLUdYmub33nMvfvXunlIyVLl1a5XayMVm5jdnUVM5zuSCXzHOeF9WrV0fNmjXx8OFDXLp0Ca1btwbw7yXKnL1iwL+fs9TU1Hz3pep7orBkZmZiwYIFKF26NObOnVtk+6WixWSMig3ZD0Fef33/t5cK+DdJGTt2bKHcQadqf2/fvkVCQoLKnoTcpsOQxbllyxY0b95c63HpwgB+TWh6/DQ9DrIkO7fEIrebD2R/JKSkpBQovvPnzyM5ORn16tXD0qVLlZL7/BJPTcjaJD4+XmWbyHoYgbz/6CkqOf/AuHfvnrx3q6Dc3d3x8OFDHDlyBK1bt8b79+9x+vRpiEQipfFisn3Vr19fa595bQzgT0tLQ2RkJAwNDVXe5SrrOf3pp5+wbNky1KtXT+VwDdJtTMao2JDdPSS7nPdfMTExSr1iwL+XZB4/flxoseVkZ2eHu3fvIiIiAq6urkrrc5uCw97eHqGhoQgLCyuUZOzevXvySz7FiabHT9PjIEv6c0vWIiMjc43zwYMHuHPnDtq1a5dvfLJj4erqqrKXNbexYrn1yBaErE3Cw8NRr149pfWyNi5btqxSr5gQjI2NYWVlhdevXyM8PFzh5o2C6NKlC1asWIHAwEC8f/8eZ86cwbt379CwYUNUrFhRoazscxYREYHs7GytTAsjG8CvLlXnv0QiwZs3b3Ld5u3bt3j79q1WL2tT0eHdlFRstGjRAsDHAb2qki5/f3+V27Vu3RqGhoY4f/68fMxMYZLFuWfPHpXrc/urW3aTwN69ewvlMsiZM2fw6NEjtf8JTdPjp+lxkI0RjIqKQmJiotL6/fv3q9xOloD5+fkhKSkp3/hkd7qq+oFNTEzMdT+ywduqLlXnx83NDQBynaRVNgearJwukJ0XW7duVXvbSpUqoX79+khLS8OZM2dw9OhRAMp3UQIfE1UHBwckJSXh4MGDnxSzTM+ePTU657y8vOR1mJiY5FlWdnPCsmXL8OjRI4V57Kj4YDJGxUbz5s3h6OiIxMRETJs2TeFyUGBgIDZu3KjyMkb58uUxdOhQSCQSjBw5Uun2e6lUipCQECxYsEArd4MNGDAARkZGuHPnDlatWiUf8yORSPDLL78gPDxc5XbffPMNXF1dERERgbFjxyr1wGRkZODs2bOYPXv2J8coFNkTEnJLnFXR9PhpehzMzMzg4uKCjIwMLF26VD5dSlZWFjZu3Ci/S/O/2rVrBycnJ8THx2P06NHyAf0yDx8+xK5du+SvZXd9njhxQn6nMPDx5owJEyYgKytL5X4sLCxQpkwZxMfH5znRsSoDBgyAWCxGaGgoFi9eLH+iQ3Z2NjZt2oSzZ8/C0NAQw4cPV6vewjRq1CiYmZnhwIEDWLJkidJl4KSkJOzfvx9r165Vub1sbNiePXtw/vx5GBgYoGPHjirLTps2DSKRCAsXLoSvr6/SeL2oqCisW7dOfocnkbbwMiUVG3p6eli2bBkGDx6Mf/75B61atYK9vT0SExMRHR2NwYMH48yZM4iOjoa+vr7CtpMnT8br169x+PBhDBkyBOXKlUPFihWRkZGBqKgo+VizIUOGfHKcFSpUwI8//ogZM2Zg3bp12LNnDypXroyoqCikpKRg8uTJWL58ucr35+3tjTFjxuDSpUto3749bG1tYWZmhnfv3iEyMhISiURh/jGh9OjRAzExMQAg/8F68eIFmjRpIi/j4eGBUaNGaWV/mhw/TY8D8PFHecSIETh48CDOnDmDKlWq4MWLF0hNTcXs2bPx008/KW2jr68Pb29vjBgxAnfu3EGnTp0UZuBPSkpC48aN5XONOTk5oUOHDjh58iSGDx8OW1tbGBkZ4fHjxyhZsiSmTp2KxYsXK+1HJBKhY8eO8PPzQ48ePVCjRg35eKf8ekXKly+PZcuWYeLEidi6dSsOHjyIKlWqICYmBvHx8dDT08P8+fNVTvgqlAoVKmDt2rX47rvv8Ndff2Hnzp2oWrUqSpcujYSEBLx48QJSqTTXRwN17NgRP//8szyJb9WqVa43dbRu3Rrz58/Hzz//jHnz5mHJkiWws7ODSCTCq1ev5L2YP/zwQ6G8V/pysWeMipWaNWvCz88P7u7uKFmyJB4/fowyZcrg+++/x7x58+SD+/87+NjAwAC//vorNm7cKL+cFBoairi4ONjZ2eF///tfno9DUle3bt2wdetWNGnSBB8+fEBERAQcHBywadOmPJ8nZ2Vlhb1792LBggVo1KgRkpKS8ODBA7x79w4uLi7w8vLCtm3btBLjp0hOTkZSUhKSkpLkl4yzs7Ply5KSkpQuo2VlZckv+6n7Y6/p8dP0ODRp0gR//PEHGjRoAIlEgmfPnqFOnTrYtm1bnlMLVKpUCf7+/pg6dSrq1KmD169fIyIiAmZmZujRowcmTpyoUP63337D+PHjYW1tjZiYGLx58wYdOnTA/v3782yjuXPnYsiQIShbtiwePXokfyxQQbRt2xb+/v7o2rUrSpQogYcPH0IqleKbb77Brl270K9fvwLVU5QaNGiAY8eOYezYsbC3t8eLFy/w6NEj6OnpoWXLlliwYEGudxpaWFjIL1kDyndR/tegQYNw8OBB9OnTBxYWFggPD0dkZCTMzc3RpUsXrF69Gt27d9fm2yOCSKrte5GJBJKYmIimTZvCxMSkwJMyUtG5f/8+evbsiSZNmuhEQklEpCvYM0afDdk4JFV3iZHwZHeVjRgxQuBIiIh0C5MxKlYePXqEvXv3KswnJpVKcejQIaxevRoA0L9/f6HCozzcunUL1apVk0++SUREH/EyJRUrV69exZAhQ6Cvr49KlSrBzMwMUVFR8qkE+vXrh4ULFwobJNEXTp0Jhlu3bs0HX9MXj3dTUrFSvXp1eHh4ICgoSP54HLFYjGbNmqFv3755DsomoqKhzgTDtra2hRwNke5jzxgRERGRgDhmjIiIiEhATMaKEalUiqSkpFwfYEyK2F7qYXuph+2lHraXethe6inu7cVkrJgprh80obC91MP2Ug/bSz1sL/WwvdRTnNuLyRgRERGRgJiMEREREQmIyRgRERGRgJiMEREREQmIyRgRERGRgJiMEREREQmIyRgRERGRgPhsSiIiItJZIRYFf+ZwVD7rXRKOfVowhYQ9Y0REREQCYjJGREREJCAmY0REREQCYjJGREREJCCdHcAfEhICb29v3L59GxKJBNWrV8fQoUPRtWvXAm1/9epV7Nu3Dw8ePEBcXBwkEgkqVKiA+vXrY9SoUahWrVohvwMiIiKi/OlkMnb16lWMHDkShoaGcHd3h7GxMQICAjBt2jRER0dj7Nix+dZx6dIl3LhxAy4uLnBzc4OhoSEiIiJw6NAhHDlyBJs2bULTpk2L4N0QERER5U4klUqlQgeRU2ZmJjp16oRXr15h7969qF27NgAgNTUV/fv3x9OnT3H06FHY2dnlWc+HDx9QsmRJpeWXL1/GsGHD4OTkBD8/v8J4C4VGKpUiKSkJZmZmEIlEQoej89he6mF7qYftpR62l3rYXv9SZ2qL/HBqiwK6cuUKnj9/ji5dusgTMQAQi8UYP348MjMz4e/vn289qhIxAGjWrBlMTU3x/PlzrcVMREREpCmdu0wZHBwMAHBzc1Na16JFC4Uymrh16xaSk5PRoEGDPMvpWIchgH9j0sXYdBHbSz1sL/WwvdTD9lIP26twCNGeBenZ1Llk7NmzZwAAW1tbpXWmpqYwNzdHZGRkgeu7evUqgoODkZGRgcjISPzzzz8wNzfH7Nmz89wuOTlZZ0+C5ORkoUMoVthe6mF7qYftpR62l3rYXtqVlJRU5Ps0NzfPt4zOJWOpqakAAGNjY5XrxWIxXr16VeD6goOD4ePjI39ta2uLFStWwMnJKc/tTE1NC7yPoiKVSpGcnAxTU9MvfgxBQbC91MP2Ug/bSz1sL/Wwvf6V3yOO1GFmZqbF2rRH55IxbfPy8oKXlxfS0tIQHh6OtWvXYsCAAVi8eHGe02To8odfJBLpdHy6hu2lHraXethe6mF7qYftpV262pY6N4BfLBYDAN6+fatyfWpqaq69ZnkxMjKCi4sLfHx8UK1aNXz//fdISEj4pFiJiIiIPpXOJWOyKStUjQtLTk5GYmKiyvFkBWVgYIAmTZogLS0Nd+/e1bgeIiIiIm3QuWSsUaNGAICLFy8qrQsKCgIANG7c+JP28fr1awAfEzMiIiIiIelcMtasWTPY2NjgyJEjCA0NlS9PTU3F2rVrYWBggB49esiXJyQk4MmTJ0qXHK9du6bybsiLFy8iMDAQxsbGqFevXuG9ESIiIqIC0LmuIQMDAyxatAgeHh4YOHAgunTpArFYjICAALx48QKTJk1C1apV5eV37twJHx8feHp6wsvLS7583LhxMDc3h7OzMypUqIAPHz7g0aNHuHbtGgwNDbFo0SIYGRkJ8RaJiIiI5HQuGQOApk2bYteuXVizZg2OHz8uf1D4xIkT0a1btwLV4eXlhQsXLuDGjRtISEiASCRCxYoV0adPHwwdOhQ1atQo5HdBRERElD+dezYl5Y7PKlMP20s9bC/1sL3Uw/ZSD9vrX3w2JREREREVKiZjRERERAJiMkZEREQkICZjRERERAJiMkZEREQkICZjRERERAJiMkZEREQkICZjRERERAJiMkZEREQkICZjRERERAJiMkZEREQkICZjRERERAJiMkZEREQkICZjRERERAJiMkZEREQkICZjRERERAJiMkZEREQkICZjRERERAJiMkZEREQkICZjRERERAJiMkZEREQkICZjRERERAJiMkZEREQkICZjRERERAJiMkZEREQkICZjRERERAJiMkZEREQkICZjRERERAJiMkZEREQkICZjRERERAJiMkZEREQkICZjRERERAJiMkZEREQkICZjRERERAJiMkZEREQkIANNN1y8eDH69u2L6tWrazMeIiKiz1qIRecCl43KZ71LwrFPC4Z0gsY9Y9u2bUPXrl3Rv39/+Pv7Iz09XZtxEREREX0RNE7GNmzYgLZt2+LevXuYO3cu3NzcsGDBAty7d0+b8RERERF91jS+TNm6dWu0bt0aCQkJ8Pf3h5+fH/bu3Yt9+/bB0dERffv2Rbdu3SAWi7UZLxEREdFn5ZMH8FtYWMDDwwPHjx/Hjh078O233yIyMhI//fQT3NzcMGvWLNy4cUMbsRIRERF9drR6N2XDhg2xdOlSbNy4EeXKlcP79+9x8OBB/O9//0PXrl1x4sQJbe6OiIiIqNjT+DLlf6WmpuLvv/+Gr68vQkNDIZVKUa9ePfTo0QMPHjzAoUOHMHnyZERFRWHUqFHa2i0RERFRsfbJydj169exf/9+nDx5Eunp6TAxMcGgQYPQr18/1KhRQ15uwoQJGDZsGHbs2FGgZCwkJATe3t64ffs2JBIJqlevjqFDh6Jr164FjiswMBDBwcGIjo5GWloarK2t0bZtW4wZMwYmJiYav2ciIiIibdE4Gdu8eTN8fX3x7NkzSKVSuLq6ol+/fujcuTNKliypVN7CwgIdOnSAj49PvnVfvXoVI0eOhKGhIdzd3WFsbIyAgABMmzYN0dHRGDt2bL51TJw4EYmJiWjQoAG+/fZbiEQiBAcH448//kBAQAD27NkDS0tLjd47ERERkbZonIwtW7YMxsbGGDhwIPr16wcHB4d8t3FyckL37t3zLJOZmYl58+ZBJBJh586dqF27NgDgu+++Q//+/eHt7Y2OHTvCzs4uz3qGDh2K7t27w8rKSr5MKpXixx9/xO7du+Hj44MFCxbkGzMRERFRYdJ4AP/ixYtx4cIFzJ8/v0CJGPBxOowlS5bkWebKlSt4/vw5unTpIk/EAEAsFmP8+PHIzMyEv79/vvsaPXq0QiIGACKRCOPHjwcAXLt2rUAxExERERUmjXvG9PT08OzZM9SsWTPXMmFhYXjw4EG+vWE5BQcHAwDc3NyU1rVo0UKhjCYMDD6+ZX19/TzLSaVSjfdRWGQx6WJsuojtpR62l3rYXuphexUOtqd6hGgvkUiUbxmNk7HZs2fju+++yzMZO3v2LFauXKlWMvbs2TMAgK2trdI6U1NTmJubIzIyUt1w5fz8/AD8m9jlJjk5WWc/5MnJyUKHUKywvdTD9lIP20s9bC/tSkpKEjqEYkWI9jI3N8+3jMbJWEESlezsbOjpqXclNDU1FQBgbGyscr1YLMarV6/UqlMmNDQUv//+OywtLeHh4ZFnWVNTU432UZikUimSk5NhampaoEz7S8f2Ug/bSz1sL/Wwvf6V38O/1WFmZqbF2nTTl9BeWptnTJUHDx7oTFITFRWFMWPGICsrCytWrICFhUWe5XX5y0IkEul0fLqG7aUetpd62F7qYXtpF9tSPbraXmolY0OGDFF4feDAAZXjt7Kzs/Hq1StER0ejU6dOagUke5bl27dvVa5PTU3NtdcsN9HR0Rg6dCgSEhLg7e2Npk2bqrU9ERERUWFRKxnLmXiJRCJER0cjOjpaqZyenh5MTU3RsWNHzJkzR62AZFNWREZGwsnJSWFdcnIyEhMTUa9evQLX9+LFCwwZMgSvX7/GqlWr8PXXX6sVDxEREVFhUisZe/jwofz/NWvWhKenJzw9PbUaUKNGjbBhwwZcvHgR7u7uCuuCgoIAAI0bNy5QXTkTsZUrV6Jdu3ZajZWIiIjoU2k8z9i2bdvQo0cPbcYCAGjWrBlsbGxw5MgRhIaGypenpqZi7dq1MDAwUNhvQkICnjx5goSEBIV6ZIlYbGwsVqxYgW+++UbrsRIRERF9Ko0H8Be0d0pdBgYGWLRoETw8PDBw4EB06dIFYrEYAQEBePHiBSZNmoSqVavKy+/cuRM+Pj7w9PSEl5eXfPmQIUMQHR0NV1dXPHr0CI8ePVLaV87yREREREIocDJ28OBBAEC7du0gFovlrwtCnXnGAKBp06bYtWsX1qxZg+PHj8sfFD5x4kR069atQHXIxrLdvn0bt2/fVlmGyRgREREJTSQt4MymNWvWhEgkwrFjx1C1alX567xIpVKIRCKFy42kOalUiqSkJJiZmens7bm6hO2lHraXethe6mF7/SvEorPW6nJJOKa1unTVl9BeBe4ZW7x4MUQiEcqVKwcA+T5jkoiIiIjyV+BkrGfPngqvC2PwPhEREdGXRuO7KYmIiIjo02mcjMXGxuLatWtIT0+XL8vOzsbGjRvRv39/DB8+HOfPn9dKkERERESfK42ntli1ahVOnz4tn4gVANatWwdvb2/562vXrmH37t1wdnb+tCiJiIiIPlMa94zdvn0bzZo1g6GhIYCPvWI7duxAtWrVcPbsWfj6+qJUqVLYvHmz1oIlIiIi+txonIzFxcXB2tpa/vr+/ftITEzE//73P1SoUAHOzs5o164d7ty5o5VAiYiIiD5HGidjWVlZyDlF2Y0bNyASidC0aVP5svLly+PNmzefFiERERHRZ0zjMWOVKlVCSEiI/HVgYCDKlSuHatWqyZfFxcXBxMTk0yIkIhJAQSeajCpAGV2daJKIdIPGyVj79u2xfv16TJgwASVLlsSNGzcwaNAghTKPHz9G5cqVPzlIIiIios+VxsnYyJEjERQUhICAAACAg4MDPD095eufPHmCu3fvYsyYMZ8eJREREdFnSuNkTCwWY9++fQgLCwMA2NvbQ19fX76+ZMmS8PHx4bQWRERERHnQOBmTcXBwULm8cuXKvERJRERElA8+DomIiIhIQJ/UM3bp0iVs2bIFd+/exdu3b5Gdna1URiQS4cGDB5+yGyIi0mEFvfMUyP/uU955Sl8ijZOxkydPYvLkycjOzkalSpVQrVo1hTFjRERERJQ/jZOx33//HSVLlsTatWvRrFkzbcZERERE9MXQeMzY06dP4e7uzkSMiIiI6BNonIyZm5ujVKlS2oyFiIiI6IujcTLWqVMnXL58GZmZmdqMh4iIiOiLonEyNmnSJJiYmGDy5MmIiYnRZkxEREREXwyNB/B36dIFmZmZuH37NgIDA2FiYgKxWKxUTiQSITAw8JOCJCIiIvpcaZyMSaVS6Ovro2LFigrLVJUjIiIiItU0TsbOnDmjzTiIiIiIvkh8HBIRERGRgD75QeEAEB4ejoiICKSlpaF79+7aqJKIiIjoi/BJPWMhISH49ttv0bVrV0ycOBGzZ8+Wr7t27Rrq1q2L06dPf3KQRERERJ8rjZOxx48fY+jQoXjx4gWGDRuGVq1aKaxv2LAhzM3NceLEiU8OkoiIiOhzpXEy5u3tDQDw9/fHzJkz4ezsrLBeJBLB1dUVd+/e/bQIiYiIiD5jGidjwcHB6NChA2xtbXMtU7FiRcTFxWm6CyIiIqLPnsbJ2Lt372BhYZFnmYyMDGRnZ2u6CyIiIqLPnsbJWMWKFfH48eM8y9y/fx82Njaa7oKIiIjos6dxMvbVV18hKCgIly9fVrn+2LFjuH37Ntq1a6dxcERERESfO43nGRs7dixOnjyJUaNGoUePHvKxYTt37sTt27dx9OhRWFtbY/jw4VoLloiIiOhzo3EyZmFhgR07dmD69Onw9fWVL//pp58AAHXr1sXy5cthbGz86VESERERfaY+aQZ+Gxsb7NmzBw8ePMCdO3eQnJwMsVgMFxcXuLi4aCtGIiIios+WVh6HVLt2bdSuXVsbVRERERF9UQqcjOV81JE6RCIRFi9erNG2RERERJ+7AidjBw4cULlcJBJBKpXmupzJGBEREVHuCpyM/feB39nZ2fj5559x584dDBkyBA0bNoSlpSXi4+Nx7do1bN++Ha6urpgzZ47WgyYiIiL6XBQ4GbO2tlZ4vXHjRoSEhODQoUOwsrKSL69WrRoaNWqEXr16oXv37jhx4gRGjRqlvYiJiIiIPiMaT/q6f/9+dOrUSSERy6l8+fLo1KmTwrQXRERERKRI47spX716hRIlSuRZpmTJknj16pVG9YeEhMDb2xu3b9+GRCJB9erVMXToUHTt2rVA28fHx2P//v24f/8+7t27h+joaADAo0ePNIqHiIiIqDBo3DNWoUIFBAYG4sOHDyrXp6en49SpU6hQoYLadV+9ehUDBw7E9evX0aFDBwwYMACJiYmYNm0a1q9fX6A6wsPDsWLFCgQEBMDQ0BClS5dWOw4iIiKiwqZxMta7d29ERUVhwIABCAwMRGJiIgAgMTERgYGBGDhwIKKjo9GnTx+16s3MzMS8efMgEomwc+dOLFq0CDNnzsShQ4dQo0YNeHt749mzZ/nWY29vjx07duD69es4efKkRkkhERERUWHT+DKlh4cHnj17Bn9/f3h5eQEA9PT0kJ2dDQCQSqXo2bMnPDw81Kr3ypUreP78OXr27KkwkaxYLMb48eMxefJk+Pv7Y8qUKXnWU7ZsWZQtW1bNd0VERERUtDROxvT09LB48WJ0794dBw4cwKNHj5CamgqxWIyaNWuie/fuaNy4sdr1BgcHAwDc3NyU1rVo0UKhDBEREVFx98mPQ2rcuLFGSVduZJcgbW1tldaZmprC3NwckZGRWttfblRNZCs0WUy6GJsuYnuph+1VeNimBce2Ug/bSz1CtJdIJMq3jFaeTalNqampAABjY2OV68ViscZ3aKojOTlZZz/kycnJQodQrLC91MP20r6kpCShQyg22FbqYXupR4j2Mjc3z7eMziVjusLU1FToEJRIpVIkJyfD1NS0QJn2l47tpR62l6IoLdZlZmamxdp0D9tKPWwv9XwJ7aVzyZhYLAYAvH37VuX61NTUXHvNtEmXf4xEIpFOx6dr2F7qYXtpH9uz4NhW6mF7qUdX20vjqS0Ki52dHQCoHBeWnJyMxMRElePJiIiIiIojnUvGGjVqBAC4ePGi0rqgoCAA0OoNA0RERERC0rlkrFmzZrCxscGRI0cQGhoqX56amoq1a9fCwMAAPXr0kC9PSEjAkydPkJCQIES4RERERJ9E58aMGRgYYNGiRfDw8MDAgQPRpUsXiMViBAQE4MWLF5g0aRKqVq0qL79z5074+PjA09NTPvmszKxZs+T/j4uLU1o2Y8YMWFhYFPI7IiIiIsqdziVjANC0aVPs2rULa9aswfHjx+UPCp84cSK6detW4HoOHDiQ5zJPT08mY0RERCQonUzGAMDFxQV//PFHvuW8vLyUesRkHj16pO2wiIiIiLRK58aMEREREX1JmIwRERERCYjJGBEREZGAmIwRERERCYjJGBEREZGAmIwRERERCYjJGBEREZGAmIwRERERCYjJGBEREZGAmIwRERERCYjJGBEREZGAmIwRERERCYjJGBEREZGAmIwRERERCYjJGBEREZGAmIwRERERCYjJGBEREZGADIQOgIiKRohF5wKViypAGZeEY58WDBERybFnjIiIiEhATMaIiIiIBMRkjIiIiEhATMaIiIiIBMRkjIiIiEhATMaIiIiIBMSpLXQIpx4gIiL68rBnjIiIiEhA7BmjYos9iURE9DlgzxgRERGRgJiMEREREQmIyRgRERGRgJiMEREREQmIyRgRERGRgJiMEREREQmIyRgRERGRgJiMEREREQmIyRgRERGRgJiMEREREQmIyRgRERGRgJiMEREREQmIyRgRERGRgJiMEREREQmIyRgRERGRgHQ2GQsJCcGoUaPQqFEjuLq6onfv3vj777/VqiM7Oxs7duxA165d4eLigqZNm2LixIl49uxZ4QRNREREpCYDoQNQ5erVqxg5ciQMDQ3h7u4OY2NjBAQEYNq0aYiOjsbYsWMLVM+CBQuwb98+VK9eHf/73/8QHx+PY8eOISgoCHv27EH16tUL+Z0QERER5U3nkrHMzEzMmzcPIpEIO3fuRO3atQEA3333Hfr37w9vb2907NgRdnZ2edZz5coV7Nu3Dw0bNsSWLVtQokQJAED37t0xfPhw/PDDD9ixY0dhvx0iIiKiPOncZcorV67g+fPn6NKlizwRAwCxWIzx48cjMzMT/v7++dbj6+sLAJg0aZI8EQOAZs2awc3NDdeuXcPTp0+1/waIiIiI1KBzyVhwcDAAwM3NTWldixYtFMrk5erVqzAyMkL9+vWV1snqvnbt2qeESkRERPTJdO4ypWxwva2trdI6U1NTmJubIzIyMs860tLSEBcXBwcHB+jr6yutl13izGsgv1QqLXDMuqi4x1/U2F7qYXuph+1VcGwr9bC91CNEe4lEonzL6FwylpqaCgAwNjZWuV4sFuPVq1d51vH27Vt52dzqyLkvVQrSeNrmknCsyPdZnLG91MP2Ug/bq+DYVuphe6nnS2gvnbtMSURERPQl0blkTNZrJevd+q/U1NRce81kZOtz6/mSLc+t54yIiIioqOhcMiYbz6VqXFhycjISExNVjifLycjICOXKlcOLFy+QlZWltF42Viy/6TGIiIiICpvOJWONGjUCAFy8eFFpXVBQEACgcePG+dbTuHFjpKWl4ebNm0rrZHXL9kVEREQkFJ1Lxpo1awYbGxscOXIEoaGh8uWpqalYu3YtDAwM0KNHD/nyhIQEPHnyBAkJCQr19O3bFwCwatUqZGRkyJdfvnwZFy9eRKNGjVC1atVCfjdEREREeRNJdfC+2CtXrsDDwwOGhobo0qULxGIxAgIC8OLFC0yaNAnjxo2Tl/X29oaPjw88PT3h5eWlUM+8efPg6+uL6tWro3Xr1vLHIZUsWbLYPA7p0KFDuHHjBu7du4ewsDBIJBIsWbIEPXv2FDo0nRMbG4vjx4/j/PnziIiIwJs3b2Bqaor69evDw8MDdevWFTpEnZKSkoI1a9bg7t27ePHiBZKTk2Fubo6qVati0KBBaN++vSB3FRcnmzZtwm+//QYA2Lt3L1xdXYUNSMe0adMG0dHRKtf169cPCxcuLOKIiodTp05h165dePDgAdLT01G2bFm4urpi+vTpqFixotDh6QR/f3/Mnj07zzJNmzbF1q1biyiiT6NzU1sAHxtw165dWLNmDY4fPw6JRILq1atj4sSJ6NatW4HrWbhwIRwdHbF3715s374dRkZG+PrrrzF58uRi0yu2evVqREdHw9zcHFZWVrl+sRGwfft2bNq0CVWqVEHz5s1haWmJyMhIBAYGIjAwEMuXL0fnzp2FDlNnJCYmws/PD3Xr1kXbtm1hZmaG+Ph4/PPPP5gwYQL69u2Ln376SegwddaTJ0+wZs0aGBkZIS0tTehwdJaxsTGGDh2qtNzJyUmAaHSbVCrFggULsHfvXlSpUgWdO3dGmTJl8Pr1a1y7dg3R0dFMxv5frVq14OnpqXLdyZMn8fjxY5WTx+sqnewZo39dunQJtra2sLa2xsaNG7F8+XL2jOUiICAAFhYWaNiwocLy69evY9iwYShTpgwuXLig8HisL1lWVhakUikMDBT/JktNTUW/fv0QHh6OI0eOoEaNGgJFqLuysrLQr18/iEQi2NnZ4fDhw+wZU6FNmzYAgDNnzggcSfGwbds2/Pzzzxg0aBDmzp2rNGl5Zmam0vlKijIyMtCyZUukpqbi3LlzKFu2rNAhFYjOjRkjRc2bN4e1tbXQYRQL7du3V0rEAKBhw4Zo0qQJkpKS8OjRIwEi0036+voqv9jFYrH8L8r8nnbxpdq0aRMePnyIxYsXq3zKB5G63r9/j99//x02NjaYM2eOys8VE7H8BQYGIikpCV999VWxScQAHb1MSaRtsi8xfpnl78OHD7hy5QpEIlGxGFdZ1MLCwuDj44Nx48ax17AAMjIycODAAcTGxsLExAT169dHzZo1hQ5L5wQFBSEpKQk9evRAdnY2AgIC8OzZMxgbG6N58+b5TulEH+3fvx8A0KdPH4EjUQ9/meizFxMTg0uXLqFcuXJwcHAQOhydk5KSgq1btyI7Oxvx8fE4f/48Xr58CU9PT87F9x+ZmZmYNWsW7O3tMXr0aKHDKRbi4uIwa9YshWUtW7bEsmXLYGFhIVBUuufevXsAPvZYd+vWDU+fPpWv09PTw7BhwzBz5kyhwisWoqOjcfnyZZQvXx4tW7YUOhy1MBmjz5pEIsGMGTOQkZGBadOm8ZKSCikpKfDx8ZG/NjQ0xIwZMzBixAgBo9JN69evx6NHj7Bv3z4YGhoKHY7O69mzJxo3bozq1aujRIkSePLkCXx8fHD+/HmMHz8eu3fv5h27/y8+Ph4AsGXLFtSuXRu+vr6wt7dHaGgo5s+fj82bN8PGxgYDBw4UOFLd5e/vj+zsbPTs2bPYfddzzBh9trKzszFnzhxcu3YNffv2Rffu3YUOSSdVrlwZjx49woMHD3D69GlMmDABK1euhJeXFzIzM4UOT2c8fPgQ69evx4gRI1CnTh2hwykWPD090bhxY1hYWEAsFqNu3brYsGEDGjRogFu3buHcuXNCh6gzZPfSGRoa4vfff4eLiwvKlCmDhg0bYs2aNdDT08OWLVsEjlJ3ZWdnw9/fHyKRCL169RI6HLUxGaPPklQqxbx583D48GF069YNP/74o9Ah6Tx9fX1UrlwZo0ePxqRJk3Dq1Cns27dP6LB0xsyZM2FjY6M0nyGpR09PT343uKonpHypZM9KdnJyQvny5RXW1ahRAzY2Nnj+/DlSUlKECE/nBQUFISYmBk2bNoWNjY3Q4aiNyRh9dmQ9Yn5+fujSpQuWLl0KPT1+1NUhu5syODhY4Eh0x8OHDxEREQFnZ2c4OjrK/x04cADAx0lMHR0dERgYKHCkus/c3BwAkJ6eLnAkuqNatWoAPs7Lpops+fv374sspuKkuA7cl+GYMfqsZGdnY+7cufD390fnzp2xbNmyYjd2QBfExsYCANsuh969e6tcfv36dTx79gxt2rSBhYUFp6IpgJCQEABgW+XQpEkTAEBERITSOolEgufPn8PIyIg3PaiQmJiI06dPw8zMDN98843Q4WiEyRh9NnImYh07dsSvv/7KZCIPoaGhqFy5stJf4klJSVi5ciUAoFWrVkKEppN+/vlnlctnzZqFZ8+eYcyYMZz0NYfw8HBYWVnBxMREYfn169exZcsWlChRAu3btxcoOt1TpUoVuLm54eLFi/D19VXo4dm4cSNSUlLQrVs3Ts+jwqFDhyCRSNC1a9diO6k3j6qO8/X1xY0bNwB8nN9Itkx2+ahdu3Zo166dYPHpkt9//x3+/v4wMjKCnZ0d1q1bp1SmXbt2qFWrlgDR6R5/f3/s378fTZo0QaVKlVC6dGnExMTg7NmzSEtLQ4cOHdC1a1ehw6Ri6vjx4/jjjz/QrFkzWFtbo0SJEggLC0NQUBD09PTw448/olKlSkKHqVMWLFiA/v37Y968eQgMDES1atXw4MEDXLlyBdbW1pgxY4bQIeokPz8/AMX3EiXAZEzn3bhxQz4mRebmzZvyga/W1tZMxv6f7LmdaWlpWL9+vcoy1tbWTMb+X4cOHZCamorbt2/j2rVreP/+PUxNTdGgQQN0794d7u7unHaANNakSRM8efIEDx48QHBwMDIyMmBpaYnOnTtj2LBhcHFxETpEnVOlShX4+flhzZo1uHDhAoKCglC2bFkMGjQI3333HSwtLYUOUeeEhIQgLCwMLi4ucHR0FDocjfHZlEREREQC4i1mRERERAJiMkZEREQkICZjRERERAJiMkZEREQkICZjRERERAJiMkZEREQkICZjRERERAJiMkZEREQkICZjRERERAJiMkZEREQkICZjRERERAJiMkZEREQkICZjRERERAIyEDoAIqKcrl69iiFDhsDT0xNubm7w8fHBnTt38PbtWzx69AhSqRR+fn7Yv38/wsLCkJWVBXt7ewwcOBC9e/dWqOvDhw/YuXMnDh48iOjoaGRnZ8PS0hIuLi4YM2YMHB0dAQD+/v6YPXs2lixZAmNjY6xfvx7h4eEQi8Vo164dpkyZAlNTU6VYb968ifXr1+P27dtIT0+HtbU13N3d4eHhgdKlSyuUdXR0ROPGjbF69Wr8+uuvOHv2LN69e4eaNWti6tSpaNKkiUL5169fY+PGjTh//jxevXqFUqVKoXz58mjQoAGmTZsGsVgsL5uRkYGdO3fi8OHDePr0KUQiEWrVqoWRI0eibdu22jo0RFRIRFKpVCp0EEREMrJkrEWLFggODkaTJk3g6OiIly9fYsWKFZg2bRqOHDkCOzs7NGnSBCVKlEBQUBAiIiIwYsQIzJw5U17XpEmTcPz4cTg6OsrLvnz5ElevXsXUqVPRs2dPAP8mY1999RWCgoLQsWNHVKxYEdeuXcOtW7dQs2ZN7N27F6VKlZLXffLkSUyZMgWGhobo1KkTLC0tcenSJdy/fx/16tXDtm3bUKJECXl5R0dH1KxZE+/fv0eZMmXQsGFDxMfH4/jx49DT04O/vz8cHBwAAOnp6ejSpQuio6PRokULODo6QiKRICoqCpcvX8bRo0dRuXJlAB8TsZEjRyI4OBi1a9dGgwYNIJFIcO7cObx8+RLz58/H//73v6I4dESkKSkRkQ65cuWK1MHBQerg4CD19fVVWLd3716pg4ODdM6cOVKJRCJf/uHDB+mYMWOkDg4O0rt370qlUqk0JSVF6ujoKO3Zs6c0MzNToZ7MzExpcnKy/LWfn598n5cuXVIoO2vWLKmDg4PUx8dHvuzt27fShg0bSp2cnKShoaHy5dnZ2dIpU6ZIHRwcpL///rtCPbL6f/jhB2lWVpZ8+b59+6QODg7S+fPny5edPn1a6uDgIF28eLFS+7x9+1b64cMH+esVK1ZIHRwcpN7e3tLs7GyFcj179pTWqVNH+urVK6V6iEh3cMwYEemk2rVrK1123LFjB4yMjPD999/DwODfURYlSpTA5MmTAQBHjx4FAIhEIkilUpQoUQL6+voK9ejr68PExERpny1atECzZs0Ulk2aNAmGhoY4ePCgfFlgYCBSUlLQq1cv1KxZU75cJBJh2rRpMDAwwIEDB5TqNzIywrRp06Cn9+9Xb48ePWBgYIB79+4plc/ZEycjFovlPW7Z2dnYvXs3bG1t8d1330EkEimU++677yCRSHDq1CmleohId3DMGBHpJGdnZ4XX6enpCAsLg5WVFTZu3KhUPjMzEwAQEREB4GMy0rJlS1y4cAE9evRAhw4d0LBhQ7i4uChcPsypQYMGSsvKly8PGxsbREREIDU1FWKxGKGhoQCAxo0bK5WvWLEibGxs8PTpU3l5GVtbW5QpU0ahvIGBASwtLZGSkiJf1qhRI5QrVw4bNmxAaGgoWrdujQYNGsDR0VEh4Xr69CmSk5NhZWUFHx8fpVgSEhIU2oSIdBOTMSLSSWXLllV4nZKSAqlUitjYWJWJh0xaWpr8/2vWrMGGDRtw5MgRrFy5EgBQpkwZ9OrVC1OmTFEaZG9paZlrLBEREXj37h3EYjFSU1NVxihTrlw5PH36VF5extjYWGV5AwMDZGdnK5Tbs2cPvL298c8//+DcuXMAgAoVKmD06NEYNGgQACApKQkA8PjxYzx+/DjXNklPT891HREJj8kYEemknD1AAOQ9SnXq1IG/v3+B6jAyMsLkyZMxefJkREVF4erVq9izZw+2bduGDx8+YOHChQrl4+PjVdbz5s0bhRhkCZZseX7lNVG5cmX88ssvyMrKQlhYGC5evIjt27dj4cKFMDU1RZcuXeRxdOjQAWvWrNF4X0QkLI4ZI6JiQSwWw97eHhEREQqX9ArKxsYGvXv3lo87O3PmjFKZGzduKC2LjY1FVFQUqlSpIk9+atWqBQAIDg7OtbyNjY1Cr5im9PX1UatWLYwaNQorVqwAAHns9vb2EIvFuHfvHiQSySfvi4iEwWSMiIqNwYMHIz09HfPmzVO4HCkTFRWFFy9eAPg4XiokJESpTHJyMiQSCUqWLKm0LigoCJcvX1ZYtmrVKkgkEnTv3l2+rF27djA2Noa/v7/C5UGpVIrly5dDIpGgR48emr5NhIWFITo6Wmm5rMdNFruBgQEGDBiA6Oho/PLLLyoTsrCwsFx7/IhIN/AyJREVG/3798edO3dw4MAB3Lx5E82bN4eVlRXi4+MRERGBO3fuYPny5ahcuTJiY2PRp08f1KhRA7Vr10b58uWRlJSE06dPQyKRwMPDQ6n+r776CqNGjVI5z9jIkSPl5cRiMX766SdMnToVffv2RadOnWBhYYHLly/j3r17cHFxUVl/QV26dAm//PIL6tevj2rVqsHMzAxRUVE4c+YMSpUqpTBv2IQJE/DgwQNs374d586dQ6NGjWBhYYHY2FiEhYXh4cOH2Lt3b67j4YhIeEzGiKjYEIlEWLp0KVq1agVfX1+cPXsWaWlpsLCwgK2tLWbOnCmfmsLa2hpeXl64cuUKLl26hKSkJJibm6N27doYNmwY3NzclOrv0KEDevfujXXr1iEgIABisRj9+vXDlClTlKaZ6NSpk/yOx1OnTsln4B8/fjxGjRqlsuetoFq2bIno6Ghcv34dAQEBSEtLQ/ny5eWz+9vb28vLlihRAps2bcL+/ftx8OBBnDx5EhkZGShbtizs7e3Rv39/+WSyRKSbOAM/EX3xcj4OSTYrPxFRUeGYMSIiIiIBMRkjIiIiEhCTMSIiIiIBccwYERERkYDYM0ZEREQkICZjRERERAJiMkZEREQkICZjRERERAJiMkZEREQkICZjRERERAJiMkZEREQkICZjRERERAL6PwLUY8kNO3SfAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 600x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "run_direct_education_counterfactual(education_level=4, gender=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "1e95d947",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Sampling: [response]\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "f6e4de1a73974702b38c22c9230e341e",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 600x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "run_direct_education_counterfactual(education_level=7, gender=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "e0b1af2e",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Sampling: [response]\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "7e3654f282054e2e9d397693f47475a3",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 600x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "run_direct_education_counterfactual(education_level=7, gender=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "5306b8eb",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Sampling: [response]\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "2c0caa77cac8445ba1583c6aac26a853",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 600x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "run_direct_education_counterfactual(education_level=0, gender=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "e16abf15",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Sampling: [response]\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ca0eac60f73046618b72b9b59e668007",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAmMAAAE3CAYAAADmP0YBAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjkuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8hTgPZAAAACXBIWXMAAA9hAAAPYQGoP6dpAABPwklEQVR4nO3deVxN+f8H8NdtY3JTikJSRFkqjSVbGDS2YoixfmXLOlnHvsxiDMaMZdRY4svYJWUZa5axZcmSEpFkSRFKJaJb3d8f/e79uu5N3evq3Hg9Hw+Ph3vO55zzvp9zz73vPp/P+RyRVCqVgoiIiIgEoSd0AERERESfMyZjRERERAJiMkZEREQkICZjRERERAJiMkZEREQkICZjRERERAJiMkZEREQkICZjRERERAJiMkZEREQkICZjRO9x4cIFODo6YuDAgUKHUqo5OjrC0dFR6DCIiHSSgdABEAnJ398fADB27FiBIyl5kZGRCAwMRGRkJF69eoVq1arB09MTvr6+KFOmjNDhvVdoaCiSkpLQo0cPVKtWTehwEBsbi6NHj6Ju3brw8PAosePu2bMH27Ztw+3btyGVSuHg4ID+/fujW7duJRaDLhCq/lUR4rOZmZmJlStX4siRI0hJSYGpqSmaNm2KMWPGwN7evkRioA/DljH6rAUEBCAgIKDQ9V988QVq1KiBKlWqlGBUH9/evXsxYMAAHD9+HEZGRrC3t8eDBw+wfPlyDBgwANnZ2Vo9Xo0aNVCjRg2t7W/Xrl0ICAhAUlKS1vb5IWJjYxEQEICjR4+W2DF/+OEHTJ06FZGRkbC0tETlypURGRmJKVOmYO7cuSUWhy4Qov4LU9KfzadPn6J79+5Yt24dnj17htq1ayMvLw/79u2Dt7c3Ll68WCJx0IdhMkb0Hi4uLjh06BAWLVokdCha8/DhQ8yaNQt5eXmYMmUKTp48iV27duHw4cOoUaMGrl27ht9//12rxzx06BAOHTqk1X1+zvbv34+goCAYGxvj77//xsGDB3HgwAH8/fffMDY2xpYtW1jfn4np06cjKSkJjRo1wr///ovQ0FCcOnUKAwcOxOvXrzFhwgS8evVK6DCpCEzGiD4z//3vf5GTkwN3d3f4+vpCJBIBAKytrTF//nwAwI4dO/Ds2TMhw6T3WLlyJQBg1KhRaN68uXx58+bNMXLkSADAihUrBImNSs61a9dw5swZGBgY4I8//kCFChUAAIaGhpgxYwbs7e3x7Nkz7NixQ+BIqSgiqVQqFToIonfFxcXh0KFDCA8PR1JSEtLT02FmZgZXV1cMHToUDRs2LHTblJQUrFu3DqdPn0ZycjL09fVRtWpVuLu7o0+fPrCzs4O/v/97uyePHTuGatWq4cKFC/Dx8YGbmxs2bdqkVC45ORmrV6/G6dOn8eTJE5QrVw7Ozs4YOHAg2rRpo1Redlw/Pz8MHjwYy5cvx5EjR/Ds2TNUqVIF3bt3x8iRI2Fg8HGGc0qlUrRq1QpPnz7F0qVL0aVLF6UynTt3RkJCAubOnYs+ffpo5biywfu3bt1SWN6uXTskJSXh2LFjePbsGQICAhAVFQWJRIJ69eph7NixCsmG7HwUZsGCBfD29pa/zs7OxqZNm3Do0CHcu3cPeXl5sLOzQ9euXeHj4wMjIyOF7TU5P7L3oEphn5sPkZCQgM6dOwMAwsPDUbFiRYX1T58+hbu7O4CCFkltdg8X59p6W0lcH8Wt/6dPn+LQoUM4ceIE7t69i6dPn6Js2bJwdHREr1690L1790Lfd3Z2NrZu3YrDhw8jISEBEokEVlZWaNCgAb799lu4ubmp/dnUhsWLFyMwMBCtW7fGmjVrlNYHBgZi8eLF+PLLL7F9+3atHpu0iwP4SSfNnz8f586dQ/ny5VGpUiVYWloiOTkZR44cwfHjx/Hbb7+ha9euStudO3cOfn5+yMrKgqGhIWrWrAmpVIrExESsW7cOxsbGGDt2LKpUqYKGDRviypUrAKCU3BVnAHtUVBR8fX2RmZkJY2NjODg4IDU1FadPn8bp06cxZswYjB8/XuW2L168QJ8+fXD//n3Url0benp68jFbjx49wrx58zSotaIlJyfj6dOnAIBGjRqpLNOwYUMkJCQgKipKIRl7/PgxevfuDaCg5axy5cpai+vEiRNYuHAhypUrBxsbGzx48ACXL1+Gr68v1q1bh6ZNmwIATExM0LBhQ8TFxSErKwsODg4Qi8Xy/VhYWMj/n5KSgqFDhyI+Ph4GBgawtraGgYEB4uPj8fvvv+P48eNYt24dypYtqxSPOufHyckJhoaGuHfvHiwsLGBraytf5+DgoLU6komKigIA2NraKiViAFCpUiVUr14dDx48QFRUlEIydvDgQSxYsABVqlRBUFCQWsct7rX1dpwlcX0Ut/6Dg4Px559/omzZsrC0tISDgwPS0tJw8eJFXLx4EZGRkfj555+VYklOToavry/u3LkDALCzs0O5cuWQlJSEvXv34vHjx9i0aZNan01tuXr1KgDl7y8Z2fKYmBjk5eVBX19f6zGQdjAZI53Ut29fzJgxQ2E6BKlUimPHjmHKlCn46aef0LZtW4Uvu+TkZIwdOxZZWVno3r07ZsyYATMzMwBAfn4+Tp06hfz8fABAr1690KtXL/n+t23bplZ82dnZmDBhAjIzM9G5c2fMmzdPHsuuXbswa9YsrFixAq6uripbALZu3QpXV1esX78eVlZWAIDjx4/Dz88PwcHBGDJkiMJdUE+fPsW4cePUihEA6tWrhzlz5shf379/HwBgZGQES0tLldvY2NgolJXJzc1FSkqK/P/atHDhQowbNw7Dhg2Dvr4+JBIJZsyYgX/++QeLFy+Wd7PUq1cP27Ztw8CBAxEREYHZs2fLE7W35efnY/z48YiPj4enpydmzpwpT1weP36M77//HpcuXcKff/6JadOmKW2vzvlZvnw5QkNDMWPGDLRu3RoLFy5U+R5PnjyJVatWqV03PXv2RK9eveSv7927B+B/50kVWUL77jnMzs5GSkqK2i2v6lxbsuOU1PVR3Pp3c3PDhg0b0KRJE4Wk5ObNm5g0aRK2b98OT09PuLm5ydfl5eXBz88Pd+7cgZOTExYtWqRwXcbGxiIyMhJA8T+bAPDLL7/gxo0bRdb7u5YvX45KlSrJX8vOb2GfBdlyiUSC5OTk935mSFhMxkgnderUSWmZSCSCh4cHBg0ahJUrV+Lff/9VaB1bs2YNXrx4gebNm2PhwoXysVAAoKenh6+++kpr8e3btw/JycmoWLEifvvtN4WWtB49eiA6Ohpbt25FYGCgyh8bfX19/PHHH/IfGqCgu6V9+/YICwvD6dOnFb7037x5I2/FU8e7P7oZGRkAgPLlyyvUz9vKly8PoOB2+ZLi7u6OESNGyF8bGhpi5syZOHz4MKKiopCRkQFTU9Ni7+/EiROIjIyEs7MzFi1apFAPlStXxtKlS9GxY0ds374d48ePV2odU/f8FEdqaqpG57BFixYKr2XnRXaeVNH2OVT32irp66M4GjdurHJ5nTp1MGfOHAwePBj//POPQjIWFhaG69evw8LCAmvXrpWPyZKpW7cu6tatq1YcQMEwDE0+C2/evFF4XdRn4e1rJiMjg8mYDmMyRjorOTkZ+/btw/Xr1/H8+XNIJBIAQFpaGoCCv2jfTsaOHTsGABg2bFihiYa2nDlzBgDw7bffquzS9PHxwdatW+VzeBkbGyusb9WqlcpuPmdnZ4SFhSExMVFhebVq1ZTGW2lC9mVuaGhYaBnZOKrXr19/lBhU+fbbb5WWmZubw9raGnfv3kViYqJaydiRI0cAFPzwq2oFsrS0hLOzMy5cuICYmBilH2p1z09xeHt7a2XM0IecQ01jUPfaKunro7iysrJw4MABXL58GU+fPsXr168hlUqRk5MDoOA75W2y992zZ0+lROxDaGscYVGfhbfHRL77WSDdwmSMdNKuXbvw448/Kv0l+DZZKw9Q8CUr60JzdXX92OHJu4pq1aqlcr2dnR0MDQ0hkUjw4MED1KlTR2F9YX+hmpubA8BHuxVd9sMoS2xVkf0wqRpL9bFUr15d5XILCwvcvXtX7fqQJY3bt2/Hvn37VJaRncMnT54orRPq/BRHSZ9DTa4tXbw+bty4gZEjR6o83zJvf6cABTdLAECDBg3UPl5JKFOmDLKzswv9LMg+B0DJXs+kPiZjpHMePHiAOXPmQCKRYOjQoejWrRtsbGxQrlw5iEQiBAcHY/bs2Qrjll6+fCn/v4mJyUePUfZjUNigXJFIBHNzc6SkpCjEJvNuS4CMnl7BbDMf6yZnWetSZmYmpFKpylaO4nSDadsXX3yhcrmm9ZGVlQWgoDuoKKpaDIQ6P8VRnC5IbZ5DTa4tXbs+8vLyMGHCBDx58gRt2rTB8OHDUatWLZQvXx76+vq4f/8+OnTooDQWUvY5KslrQR3ly5dHdnZ2oZ+Ft5NLdVqWqeQxGSOdc/DgQUgkEnh6eqocXP3o0SOlZeXKlZP//8WLFx89IZP9WKSmpqpcL5VK5d2pb8emKW0N4JfdaZaTk4MnT54ojMmRkXUBvX1XWmkjOz/r169XGnMlFG0N4JdNH/G+rjptnkNNrq2Svj6KEh0djfv378Pa2hoBAQFKU5qo+k55OzZtj5/U1gB+W1tbpKSkFPpZkC03NDRE1apVNQuWSgSTMdI5sjmDvvzyS5Xr3x3XAQBisRiVK1fG48ePcfXqVbRq1eqjxmhnZ4ebN28iPj5e5fp79+5BIpFAX1+/0C44dWhrAH/VqlVRqVIlPH36FJcvX1Y5z5jsOLraNVMc9vb2iI2NRVxcXIkkY8UZR6WtAfwuLi4ACu6ke/bsmcp5xh48eABAO+dQk2urpK+Poupf9p1Sv359pUQMUP2dAhR0s964cQNRUVFafealtgbwN2jQABEREYXuS7bcycmJ01roOCZjpHNkY2JUzQB/584d/Pvvvyq38/DwwObNm7F+/fpiJ2Nly5bF69ev8fr1a7XGVLi7u+PQoUMIDg7G6NGjlQYpywboNmzYsNAuF3Voa/C87I7Ubdu2ISQkRCkZu3LlChISEmBoaIh27dp98PE+Ftm5KmxQcocOHbBv3z4EBQWhX79+H/3B57L9v2+QtLYG8Nvb28Pe3h537txBSEiIfMZ9mZCQEAAFc2xpa8JXda+tkr4+iqp/2edF1XeKRCLBxo0bVW7n4eGBvXv3IiQkBMOGDZNP5/E+RX02Ae0N4O/QoQPWrFmDs2fPIjk5WaH1Ky8vD7t37wYAdOzYUSvHo4+Hj0MinSObjHTbtm2IjY2VL7979y4mTJhQ6J1Dw4YNg4mJCcLDwzFz5kyF8RL5+fk4efKkUiInGygcERGhVoxeXl6oWrUqnj17hunTpyuMe9mzZ498Qs3hw4ertd+SMGzYMBgaGuLMmTNYu3atfPxNUlISZs6cCaBgHra3u0OAgmdaOjo6wtHREQ8fPizxuN8mO2+FPQT566+/hqurKxISEjBq1Cil+bZycnJw4sQJzJgxQ6vxXLt2TesPWVdl9OjRAIBVq1bh3Llz8uXnzp3D6tWrFcq8LTQ0FI6Ojmon2upeWyV9fRRV/w0aNICBgQGuXLkiT1CAgm7XyZMnF/roLw8PDzg5OSE1NRUjRoyQD+iXuXnzJrZu3aoylpJ4QLeLiwtatmyJ3NxcTJ48Gc+fPwdQkGAuWLAAd+7cgYWFhXyyZtJdbBkjnePh4QFXV1dcvXoVPXv2hJ2dHfT19XH79m1UrFgRo0ePxrJly5S2q1q1KpYvXw4/Pz+EhIRg7969sLe3R35+Ph4+fIhXr17Bz88Pbdu2lW/TuXNn3L59G6NGjYKjo6N8YsolS5YoJSNv++KLL7Bs2TIMGzYMBw4cwIkTJ1CzZk2kpqbKx5+MHj1a5RxKQrOxscG8efMwY8YM/P7779i4cSMsLCxw+/ZtSCQS1K9fH1OnThU6zPfq0qULtmzZgjVr1uDIkSOoVKkSRCIRhg8fjtatW0NPTw/+/v4YOXIkzp49iw4dOsDW1hZmZmZ4+fIl7t+/D4lEonIGe03Ur18fdnZ2uHfvHr766ivUqFEDhoaGqFOnDmbNmqWVY7yta9euiIiIwI4dOzB48GD5nFuyWeL79u2rsgtaU+peWyV9fRRV/5UqVYKPjw/WrVuHadOm4c8//0SFChVw584d5OXlYdasWfjpp5+U9quvrw9/f38MHToUUVFR6Ny5s8IM/Onp6XBzc0P//v3l2xT12dS2BQsWoF+/frh8+TLatm2LmjVr4tGjR0hLS0OZMmWwdOnSEhmXRx+GyRjpHAMDA/z3v//FsmXLcPjwYTx48AAWFhbo1asXxo0bJ5/DSJUWLVpg3759+O9//4vTp08jISEBZcuWhY2NDdzd3fHNN98olB8xYgTy8/Oxf/9+xMfHy28Ff9+UGjINGjTAnj17sHr1apw5cwa3bt2CsbEx3N3d4ePjo5OJmEz37t1RvXp1BAYGIjIyEvHx8ahWrRq8vLwwfPhwld16stYDCwuLQmfvLymNGzfG4sWLsWHDBsTHx8unUujRo4e8jKWlJYKCgrBz504cOHAAcXFx8olIXVxc0KJFC/kzHj+Unp4eVq9ejSVLluDSpUuIjo5GXl6eVvZdmF9++QWNGjXCtm3b5HeNurq6ol+/foU+Z1F2Dt+dSqI41L22SvL6KE79T506FZUrV8b27duRmJiI7OxsNG/eHKNHj37vo4qqVq2K0NBQbN68GYcOHcLdu3chlUphZWWFtm3bKtxcARTvs6lNVlZW2LVrF1asWIGjR48iLi4O5cuXh6enJ8aMGVPo9CKkW/igcCIqlvXr12PhwoUYO3Ys/Pz8hA6HNDBq1Cj8+++/2Lx5M5o0aSJ0OET0/zhmjIiK5cqVKyhbtqxClwyVLlevXoWTkxMTMSIdw2SMiIolMjIS33zzjXwWdCpdEhIS8Pz5cwwZMkToUIjoHeymJCIiIhIQW8aIiIiIBMRkjIiIiEhATMaIiIiIBMRkjIiIiEhATMaIiIiIBMRkjKiEyZ7xqMsP4v4cfMrnQfYMyunTpwsdSrH5+/vD0dER/v7+Qoeitnbt2unEM1up9OLjkIhILa9fv8bp06cRHR2N6OhoxMTEICsrC9WrV8eRI0eEDu+Tl5mZiQ0bNsDExASDBw8WOhwqYXv27MG2bdtw+/ZtSKVSODg4oH///ujWrZvQodEHYDJGRGq5e/cuH4ckoMzMTAQEBMDa2rrQZMzExAQ1atR478PuqfT54YcfEBQUBACoWbMmRCIRIiMjERkZiatXr+KHH34QOELSFJMxIlKLgYEBXF1d4ezsDGdnZ+Tm5mLmzJlCh0Vv+frrr/H1118LHQZp0f79+xEUFARjY2OsWLECzZs3BwCcO3cOY8aMwZYtW+Dm5oZOnToJHClpgmPGiEgttWvXRlBQEGbPno1vvvkG1apVEzokok/eypUrARQ87F2WiAFA8+bNMXLkSADAihUrBImNPhyTMSp1Hjx4gEmTJqFZs2Zo0KABvvnmG2zbtg3A+wfSSqVS7N+/H0OGDEHTpk3h5OSE9u3bY968eXj69KlS+QsXLsDR0REDBw5Efn4+NmzYAC8vLzg7O6NFixaYOXMm0tLSCo0zIiICgwcPRsOGDdGoUSMMHDgQ4eHhRb6/7OxsBAYGwtvbGw0bNpS/x7Vr1yInJ0ep/NsDn9PS0jB37ly0a9cO9evXL1UDuIui7vmTUfc8vH3eVSlq4H92djb++9//onfv3mjcuDEaNGiADh06YMqUKYiIiFAom5iYiMDAQAwcOBBt2rSBk5MTmjVrhmHDhuHEiRNK+54+fTrat28PAEhKSoKjo6PCP5miBvDfvn0bU6ZMQevWreHk5IQWLVpg7NixuHr1qsry06dPh6OjI0JDQ5GSkoIZM2bA3d0dzs7O8PT0xJYtW1Rup02PHz/GvHnz0LFjR7i4uKBx48YYOHAgDh06pFAuLy8PLVu2hKOjI65du1bo/hYuXAhHR0csWLBAaV10dDQmTpyIVq1ayetn3LhxuHHjhtbfV3EkJCTg9u3bAICePXsqrZctu3XrFu7evVuisZF2MBmjUuXmzZvo2bMn9u/fj+zsbNjb2+PFixf46aefMG/evEK3k0gkmDBhAiZNmoSzZ8/CyMgI9vb2SE1NxaZNm9CjR4/3folNmTIF8+fPh0Qiga2tLTIyMhASEgIfHx+VCdL+/fsxaNAgnDt3DoaGhrCzs0NcXBx8fX1x4MCBQo+TkpKCXr16YfHixbh16xYqVqwIa2trxMfH4/fff8fgwYPx+vVrldumpaWhZ8+e2L59O8RiMezt7aGnp1uXeJ8+fdC6dWscPHhQre00PX+angdNJScno2fPnli0aBGioqJgYWEBe3t7ZGRkYO/evUp3Cq5atQqLFy9GTEwMypYtC0dHRxgYGODMmTMYOXIkAgMDFcrb2dnByckJAGBkZISGDRsq/CuOY8eOwdvbG3v37kV2djYcHR0hlUoRFhaGfv36YceOHUW+v/3798PS0hJmZmaIj4/H3Llz5S03H0NERAS8vLywadMmPH78GLa2thCLxYiIiMD48ePx22+/ycvq6+ujc+fOAArOvypSqVR+/j09PRXW/f333+jduzcOHDiAnJwc1K5dG3l5eTh8+DB69+6NsLCwj/QuCxcVFQUAsLW1RcWKFZXWV6pUCdWrV1coS6ULx4xRqZGfn48pU6YgMzMTrVu3xh9//AFTU1MAwOHDhzF58mQU9tz75cuX49ChQ6hXrx7mz5+PunXrAii4M/C3337D1q1bMXnyZISEhChtGxkZiQoVKiA4OBguLi4ACgaxDx48GLdv30ZISAj69esnL5+SkoLZs2cjPz8fI0aMwPjx42FgYACJRII//vgDy5cvL/T9jR8/HvHx8fD09MTMmTPlX7yPHz/G999/j0uXLuHPP//EtGnTlLYPCgqCi4sLtm3bhsqVKwMA3rx5I18/bty497YgFUbW6qgNT58+RUpKCrKzs9XaTpPzp+l50FReXh78/Pxw584dODk5YdGiRbC3t5evj42NRWRkpMI2HTp0QO/eveHi4gKRSCRffunSJUyYMAHLli1Dp06d5D+0o0aNgpeXF9q3b49KlSqpfW5SUlIwdepU5OTkwMfHB1OmTIGRkRHy8/MRGBiIpUuXYu7cuXBxcUGdOnWUtl+1ahXatWuHefPmoXz58gCArVu34ueff8bKlSsxYMAA+XJtSUlJwdixY5GVlYVJkyZhyJAhMDIyAgBcuXIFEyZMwLp16+Dm5oa2bdsCgDxxO3DgAKZOnar0R8nFixeRkpICW1tb+TUNAKdOncLChQthZmaGuXPnokOHDvJ1wcHB+OmnnzB9+nS4urrC0tKyyNhPnjyJVatWqf2ee/bsiV69eslf37t3DwBgY2NT6DY2NjZ48OAB7t+/r/bxSHi69Wcz0XuEh4cjLi4OZmZmWLJkiTwRA4COHTtixIgRkEgkStulpaXh77//hlgsxsqVK+U/5ABQtmxZzJkzB87OzoiJicGlS5eUtpdIJJgzZ47Cl3aNGjXg6+sLoOAL/G3btm3Dq1ev4OzsjO+//x4GBgV/8xgaGmLGjBmoWbOmyvd34sQJREZGwtnZGYsWLVL4C7hy5cpYunQpjI2NsX37dpWtY/r6+vjzzz/liRgAlClTRv7/mJgYXLlyRe1/QtP0/Gl6HjQVFhaG69evw8LCAmvXrlVIxACgbt266N+/v8KyNm3aoEGDBgqJGAA0btwY48ePR15enlZb8LZu3YqsrCzUrVsXs2bNkic1enp6GDVqFNq0aQOJRIJ169ap3N7MzAwLFy5USLj69++P+vXr482bN7hw4YLWYpVZv3490tPTMWjQIIwcOVIeMwA0bNgQP//8M4CCFi0ZV1dX2NjYICUlReU1vW/fPgDKrWLLli2DVCrFr7/+qpCIAcC3334LHx8fvHz5EsHBwcWKPTU1VaNr7tGjRwr7yczMBID3JrqydbKyVLqwZYxKjbNnzwIoaE0wMTFRWu/t7Y2AgACl5SdPnkROTg7atWunkKjI6Onp4auvvsK1a9cQERGBxo0bK6w3NTVV+mIGAGdnZwBQGp925swZAFBoLXtb//798eOPPyotl83R1aNHD3ni8DZLS0s4OzvjwoULiImJUYqzRYsWsLKyUnlMADh+/Hih60qKJjFoev40PQ+aOnbsGICCVo0KFSoUe7u0tDT8888/iI6ORmpqqrw1MysrC0BB17y2yMbKDRgwQOV6Hx8fnDx5stAxdZ6enjA2NlZa7uTkhOvXryMxMVFrscrIugW//fZbletbtWoFQ0NDREZGIjc3V37teHp6YtWqVdi3bx/c3Nzk5XNzc3H48GEABS1oMklJSfJkWjYu713t2rXDunXrcPHixWLF7u3tDW9v72KVfR/ZZ8LQ0LDQMrIktbBhDKTbmIxRqSFrqn97oPLbrK2tIRaL5T9iMnFxcQAKxlIU9sOcmpoKoKBL5F2FdQ1YWFgAAF6+fKkyzndbRmQKW37r1i0AwPbt2+V/ub9Ltu8nT54ordN2S4+u0PT8aXoeNJWQkAAAaNCgQbG3OXPmDCZMmIAXL14UWiYjI+ODY5OR1UmtWrVUrq9duzYA4NmzZ8jKyoJYLFZYL+sufZfsWnj16pWWIi3w8uVLJCUlAQDmzJnz3rJv3rxBenq6vEW5a9euWLVqFQ4fPow5c+bIE5nw8HCkp6ejbt26Cp8B2efszZs3hX7OZEmRqu+Jj0nWwq2q5V9GNna1bNmyJRITaReTMSo1ZOOMypUrV2iZcuXKKSVjsh+6R48eKTX/v+vtMVYyqloCACh1LcnIfpDMzc1Vrpf9cL1LFrfsR+F9VP31W1icpZ2m50/T86Ap2fkr7pipzMxMTJo0CS9evED37t3Rv39/1KhRA2KxGHp6ejh79iyGDBmC3NxcrcUoq5PC3vvby1++fKmUjH3xxRcqt5ONySpszKam3r6Wi9Nl/vZ1UatWLdSpUwc3b97E2bNn0aZNGwD/66J8u1UM+N/nLCsrq8hjqfqe+JiK0wVZnK5M0l1MxqjUkP0QvO+v73dbqYD/JSmjRo3CxIkTP05w7xzvxYsXSEtLU9mSUNh0GLI4169fjxYtWmg9Ll0YwK8JTc+fpudBlmQXllgUdvOB7I+E4o7ZOXXqFDIyMvDll19i4cKFSsl9UYmnJmR1kpqaqrJOZC2MwPv/6Ckpb/+BERMT895uOlU8PT1x8+ZN7Nu3D23atMHr169x7NgxiEQipfFismM1bNhQa595bQ3gt7OzA4D3dgPL1tna2qp9PBIekzEqNWRfSLLuvHclJycrtYoB/+uSkc3T87HZ2dnh2rVrSEhIgKurq9L6O3fuqNzO3t4esbGxiIuL+yjJWExMjLzLpzTR9Pxpeh5kSX9hyVphd6vVqlULN27cQFRUFDw8PIqMT3YuXF1dVbayFjZWrLAW2eKQ1Ul8fDy+/PJLpfWyOq5YsaJSq5gQTExMYGlpiSdPniA+Pl7h5o3i8PLywpIlS3D06FG8fv0ax48fx8uXL9G4cWNUqVJFoazsc5aQkID8/HytTAsjG8Cvrnevf9nNQ/fv38ezZ8+Uprd4+vQpHjx4AEC9bnLSHbybkkqNli1bAigY0Ksq6QoNDVW5XZs2bWBoaIhTp07Jx8x8TLI4t2/frnJ9YX91y24SCAoK+ijdIMePH8etW7fU/ic0Tc+fpudBNkYwMTERz58/V1q/c+dOldvJErCQkBCkp6cXGZ9sHNCzZ8+U1j1//rzQ48jGBGkyUNvd3R0ACp2kddOmTQrldIHsutiwYYPa21atWhUNGzbEq1evcPz4cfm8Y++2igEFiaqDgwPS09Oxe/fuD4pZxtvbW6NrbuzYsQr7sbe3l49vUzX9jmyZg4MDatSooZXYqWQxGaNSo0WLFnB0dMTz588xefJkhe6go0ePIjAwUGU3hpWVFQYNGgSJRIJhw4Yp3X4vlUoRHR2NH3/8USt3g/Xr1w/GxsaIiorCsmXL5GN+JBIJfvvtN8THx6vc7uuvv4arqysSEhIwatQopRaYnJwcnDhxAjNmzPjgGIUie0JCYYmzKpqeP03Pg5mZGVxcXJCTk4OFCxfKB03n5eUhMDBQfpfmuzw8PODk5ITU1FSMGDFCPqBf5ubNm9i6dav8teyuz0OHDsnvFAYKbs4YN24c8vLyVB7H3Nwc5cqVQ2pqaqGte4Xp168fxGIxYmNjMX/+fPmg7/z8fKxZswYnTpyAoaEhhgwZotZ+P6bhw4fDzMwMu3btwoIFC5S6gdPT07Fz585CHwUkGxu2fft2nDp1CgYGBoU+v3Hy5MkQiUSYO3cugoODlcbrJSYmYuXKlYJM/Dp69GgABXO9nTt3Tr783LlzWL16tUIZKn3YTUmlhp6eHhYtWoSBAwfi33//RevWrWFvb4/nz58jKSkJAwcOxPHjx5GUlAR9fX2FbSdOnIgnT55g79698PHxQaVKlVClShXk5OQgMTFRPtbMx8fng+OsXLkyfv75Z0ydOhUrV67E9u3bUa1aNSQmJiIzMxMTJ07E4sWLVb4/f39/jBw5EmfPnkWHDh1ga2sLMzMzvHz5Evfv34dEIlE5A3dJ69GjB5KTkwFA/oP18OFDNG3aVF7G19cXw4cP18rxNDl/mp4HoOBHeejQodi9ezeOHz+O6tWr4+HDh8jKysKMGTPwyy+/KG2jr68Pf39/DB06FFFRUejcuTPs7OxQrlw5JCUlIT09HW5ubvK5xpycnNCxY0ccPnwYQ4YMga2tLYyNjXH79m2UKVMG33//PebPn690HJFIhE6dOiEkJAQ9evRA7dq15eOdZC1bhbGyssKiRYswfvx4bNiwAbt370b16tWRnJyM1NRU6OnpYc6cOSonfBVK5cqVsWLFCnz33Xf4+++/sWXLFtSoUQNffPEF0tLS8PDhQ0ilUnTp0kXl9p06dcKvv/4qT+Jbt25d6E0dbdq0wZw5c/Drr79i9uzZWLBgAezs7CASifD48WN5K+ZPP/30Ud7r+3Tt2hURERHYsWMHBg8eLG8pkyXkffv2LbQOSPcxGaNSpU6dOggJCcGyZcsQHh6O27dvw9bWFj/88AMGDBggv1Pq3cHHBgYG+P333+Hl5YUdO3YgKioKsbGxKF++POzs7PDll1+iY8eOWmvi79atG6ysrPDXX3/Jxy3Vr18fI0eOhJ2dXaFJgKWlJYKCgrBz504cOHAAcXFxSE5ORsWKFeHi4oIWLVrIH/UipIyMDKWuuPz8fIVl73aj5eXlybv91P2x1/T8aXoemjZtirVr18Lf3x83btzAvXv30KBBA3z33XewsrJSmYwBBd1ioaGh2Lx5Mw4dOoS7d+9CKpXCysoKbdu2VRiUDQB//PEH7O3tsWfPHiQnJ8PMzAwdO3aEn5/fe2+2mDVrFsqVK4djx47h1q1b753y4F3t27dHaGgoAgMDcf78edy8eRMmJib4+uuvMWzYMJVjyYTWqFEjHDhwABs2bMCJEyfw4MED5Ofnw8rKCq1atULbtm1VzgUIFLQktmzZEidPngSgfBfluwYMGIAmTZpg48aNOH/+POLj42FkZITKlSujWbNm+Prrr+V3Zpa0X375BY0aNcK2bdvkd127urqiX79+6N69uyAxkXaIpNq+F5lIIM+fP0ezZs1Qvnz5Yk/KSCXn+vXr8Pb2RtOmTbFx40ahwyEi0hkcM0afDNk4JF38y57+N0/U0KFDBY6EiEi3MBmjUuXWrVsICgpSmE9MKpViz549+PPPPwEUjJ0g3RMZGYmaNWsK1sVDRKSr2E1JpcqFCxfg4+MDfX19VK1aFWZmZkhMTJSPVerTpw/mzp0rbJBEnzl1Jhhu06YNRo0a9ZEjItJtHMBPpUqtWrXg6+uL8PBw+eNxxGIxmjdvjt69e/NuIiIdoM4Ew5wxnogtY0RERESC4pgxIiIiIgExGStFpFIp0tPTC32AMSlifamH9aUe1pd6WF/qYX2pp7TXF5OxUqa0ftCEwvpSD+tLPawv9bC+1MP6Uk9pri8mY0REREQCYjJGREREJCAmY0REREQCYjJGREREJCAmY0REREQCYjJGREREJCAmY0REREQC4rMpiYiISGdFmxf/mcOJRax3STvwYcF8JGwZIyIiIhIQkzEiIiIiATEZIyIiIhIQkzEiIiIiATEZIyIiIhIQkzEiIiIiATEZIyIiIhIQkzEiIiIiATEZIyIiIhIQkzEiIiIiATEZIyIiIhIQkzEiIiIiATEZIyIiIhIQkzEiIiIiATEZIyIiIhIQkzEiIiIiATEZIyIiIhIQkzEiIiIiATEZIyIiIhIQkzEiIiIiATEZIyIiIhIQkzEiIiIiATEZIyIiIhIQkzEiIiIiATEZIyIiIhIQkzEiIiIiATEZIyIiIhIQkzEiIiIiATEZIyIiIhIQkzEiIiIiATEZIyIiIhIQkzEiIiIiATEZIyIiIhIQkzEiIiIiATEZIyIiIhIQkzEiIiIiARkIHQAREdHnJNq8S7HLJhax3iXtwIcFQzpBZ5Ox6Oho+Pv74+rVq5BIJKhVqxYGDRqErl27Fmv7S5cu4ejRo4iIiEBSUhJevXoFa2trtG/fHiNHjkT58uU/8jsgIiIiKppOJmMXLlzAsGHDYGhoCE9PT5iYmCAsLAyTJ09GUlISRo0aVeQ+xo8fj+fPn6NRo0b45ptvIBKJEBERgbVr1yIsLAzbt2+HhYVFCbwbIiIiosLpXDKWm5uL2bNnQyQSYcuWLahXrx4A4LvvvkPfvn3h7++PTp06wc7O7r37GTRoELp37w5LS0v5MqlUip9//hnbtm1DQEAAfvzxx4/5VoiIiIiKpHMD+M+fP48HDx7Ay8tLnogBgFgsxpgxY5Cbm4vQ0NAi9zNixAiFRAwARCIRxowZAwC4ePGidgMnIiIi0oDOJWMREREAAHd3d6V1LVu2VCijCQODgsZAfX19jfdBREREpC0610157949AICtra3SOlNTU1SoUAH379/XeP8hISEA/pfYFUYqlWp8jI9FFpMuxqaLWF/qYX2ph/WlHtbXx8H6VI8Q9SUSiYoso3PJWFZWFgDAxMRE5XqxWIzHjx9rtO/Y2Fj89ddfsLCwgK+v73vLZmRk6OyHPCMjQ+gQShXWl3pYX+phfamH9aVd6enpQodQqghRXxUqVCiyjM4lYx9LYmIiRo4ciby8PCxZsgTm5ubvLW9qalpCkRWfVCpFRkYGTE1Ni5Vpf+5YX+phfamH9aUe1tf/FDV3mDrMzMy0uDfd9DnUl84lY2KxGADw4sULleuzsrIKbTUrTFJSEgYNGoS0tDT4+/ujWbNmRW6jy18WIpFIp+PTNawv9bC+1MP6Ug/rS7tYl+rR1frSuQH8sikrVI0Ly8jIwPPnz1WOJyvMw4cPMXDgQDx58gTLli1D27ZttRUqERER0QfTuZaxJk2aYPXq1Thz5gw8PT0V1oWHhwMA3NzcirWvhw8fwsfHB0+ePMHSpUvh4eGh9XiJ6NNU3EfWFKcLhY+sIaL30bmWsebNm8PGxgb79u1DbGysfHlWVhZWrFgBAwMD9OjRQ748LS0Nd+7cQVpamsJ+ZIlYSkoKlixZgq+//rrE3gMRERFRcelcy5iBgQHmzZsHX19f9O/fH15eXhCLxQgLC8PDhw8xYcIE1KhRQ15+y5YtCAgIgJ+fH8aOHStf7uPjg6SkJLi6uuLWrVu4deuW0rHeLk9EREQkBJ1LxgCgWbNm2Lp1K5YvX46DBw/KHxQ+fvx4dOvWrVj7SEpKAgBcvXoVV69eVVmGyRgREREJTSeTMQBwcXHB2rVriyw3duxYlUmVqpYwIiIiIl2jc2PGiIiIiD4nTMaIiIiIBKRxMjZ//nzEx8drMxYiIiKiz47GydjGjRvRtWtX9O3bF6GhocjOztZmXERERESfBY2TsdWrV6N9+/aIiYnBrFmz4O7ujh9//BExMTHajI+IiIjok6bx3ZRt2rRBmzZtkJaWhtDQUISEhCAoKAg7duyAo6MjevfujW7dusmfNUlEREREyj54AL+5uTl8fX1x8OBBbN68Gd988w3u37+PX375Be7u7pg+fTouX76sjViJiIiIPjlavZuycePGWLhwIQIDA1GpUiW8fv0au3fvxn/+8x907doVhw4d0ubhiIiIiEo9rU36mpWVhX/++QfBwcGIjY2FVCrFl19+iR49euDGjRvYs2cPJk6ciMTERAwfPlxbhyUiIiIq1T44Gbt06RJ27tyJw4cPIzs7G+XLl8eAAQPQp08f1K5dW15u3LhxGDx4MDZv3sxkjIiIiOj/aZyMrVu3DsHBwbh37x6kUilcXV3Rp08fdOnSBWXKlFEqb25ujo4dOyIgIOCDAiYiIiL6lGicjC1atAgmJibo378/+vTpAwcHhyK3cXJyQvfu3TU9JBEREdEnR+NkbP78+ejSpQvKli1b7G1k02EQERERUQGN76bU09PDvXv33lsmLi4Ou3fv1vQQRERERJ88jVvGZsyYge+++w516tQptMyJEyewdOlSdk0SEX3Cos27FLtsYhHrXdIOfFgwRKWQxi1jUqm0yDL5+fnQ09PqVGZEREREn5SPminduHEDpqamH/MQRERERKWaWt2UPj4+Cq937dqFiIgIpXL5+fl4/PgxkpKS0Llz5w+LkIiIiOgTplYy9nbiJRKJkJSUhKSkJKVyenp6MDU1RadOnTBz5swPj5KIiIjoE6VWMnbz5k35/+vUqQM/Pz/4+flpPSgiIiKiz4XGd1Nu3LgR1tbW2oyFiIiI6LOjcTLm5uamzTiIiIiIPkvFTsZkk7d6eHhALBarNZkr5xkjIiIiUq3Yydj06dMhEonQoEEDiMVi+ev3kUqlEIlETMaIiIiIClHsZGz+/PkQiUSoVKkSAGDBggUfLSgiIiKiz0WxkzFvb2+F1z169NB6MERERESfGz6riIiIiEhAGidjKSkpuHjxIrKzs+XL8vPzERgYiL59+2LIkCE4deqUVoIkIiIi+lRpPLXFsmXLcOzYMYSHh8uXrVy5Ev7+/vLXFy9exLZt2+Ds7PxhURIRERF9ojRuGbt69SqaN28OQ0NDAAWtYps3b0bNmjVx4sQJBAcHo2zZsli3bp3WgiUiIiL61GicjD19+lRhBv7r16/j+fPn+M9//oPKlSvD2dkZHh4eiIqK0kqgRERERJ8ijZOxvLw8SKVS+evLly9DJBKhWbNm8mVWVlZ49uzZh0VIRERE9AnTOBmrWrUqoqOj5a+PHj2KSpUqoWbNmvJlT58+Rfny5T8sQiIiIqJPmMYD+Dt06IBVq1Zh3LhxKFOmDC5fvowBAwYolLl9+zaqVav2wUESERERfao0TsaGDRuG8PBwhIWFAQAcHBzg5+cnX3/nzh1cu3YNI0eO/PAoiYiIiD5RGidjYrEYO3bsQFxcHADA3t4e+vr68vVlypRBQEAAp7UgIiIieg+NkzEZBwcHlcurVavGLkoiIiKiIvBxSEREREQC+qCWsbNnz2L9+vW4du0aXrx4gfz8fKUyIpEIN27c+JDDEBEREX2yNE7GDh8+jIkTJyI/Px9Vq1ZFzZo1FcaMEREREVHRNE7G/vrrL5QpUwYrVqxA8+bNtRkTERER0WdD4zFjd+/ehaenJxMxIiIiog+gcTJWoUIFlC1bVpuxEBEREX12NE7GOnfujHPnziE3N1eb8RARERF9VjROxiZMmIDy5ctj4sSJSE5O1mZMRERERJ8NjQfwe3l5ITc3F1evXsXRo0dRvnx5iMVipXIikQhHjx79oCCJiIiIPlUaJ2NSqRT6+vqoUqWKwjJV5YiIiIhINY2TsePHj2szDiIiIqLPEh+HRERERCSgD35QOADEx8cjISEBr169Qvfu3bWxSyIiIqLPwge1jEVHR+Obb75B165dMX78eMyYMUO+7uLFi2jQoAGOHTv2wUESERERfao0TsZu376NQYMG4eHDhxg8eDBat26tsL5x48aoUKECDh069MFBEhEREX2qNE7G/P39AQChoaGYNm0anJ2dFdaLRCK4urri2rVrHxYhERER0SdM42QsIiICHTt2hK2tbaFlqlSpgqdPn2p6CCIiIqJPnsYD+F++fAlzc/P3lsnJyUF+fr5G+4+Ojoa/vz+uXr0KiUSCWrVqYdCgQejatWuxtk9NTcXOnTtx/fp1xMTEICkpCQBw69YtjeIhIiIi+hg0TsaqVKmC27dvv7fM9evXYWNjo/a+L1y4gGHDhsHQ0BCenp4wMTFBWFgYJk+ejKSkJIwaNarIfcTHx2PJkiUQiUSwtbXFF198gezsbLVjISIiIvqYNO6m/OqrrxAeHo5z586pXH/gwAFcvXoVHh4eau03NzcXs2fPhkgkwpYtWzBv3jxMmzYNe/bsQe3ateHv74979+4VuR97e3ts3rwZly5dwuHDh1G5cmW14iAiIiIqCRonY6NGjUKlSpUwfPhwzJkzBzExMQCALVu2YMqUKZg8eTKsra0xZMgQtfZ7/vx5PHjwAF5eXqhXr558uVgsxpgxY5Cbm4vQ0NAi91OxYkU0adJE5fMyiYiIiHSFxt2U5ubm2Lx5M6ZMmYLg4GD58l9++QUA0KBBAyxevBgmJiZq7TciIgIA4O7urrSuZcuWCmWIiIiISrsPmoHfxsYG27dvx40bNxAVFYWMjAyIxWK4uLjAxcVFo33KuiBV3aVpamqKChUq4P79+x8SdrHo4gPOZTHpYmy6iPWlHtbXx8M6LT7WlXpYX+oRor5EIlGRZbTyOKR69eopdCl+iKysLAAotEVNLBbj8ePHWjnW+2RkZOjshzwjI0PoEEoV1pd6WF/al56eLnQIpQbrSj2sL/UIUV8VKlQoskyxk7G3H3WkDpFIhPnz52u0rZBMTU2FDkGJVCpFRkYGTE1Ni5Vpf+5YX+phfSlK1OK+zMzMtLg33cO6Ug/rSz2fQ30VOxnbtWuXyuUikUhlC5JsubrJmGzA/YsXL1Suz8rKUnscmiZ0+cdIJBLpdHy6hvWlHtaX9rE+i491pR7Wl3p0tb6KnYy9+8Dv/Px8/Prrr4iKioKPjw8aN24MCwsLpKam4uLFi9i0aRNcXV0xc+ZMtQKys7MDANy/fx9OTk4K6zIyMvD8+XN8+eWXau2TiIiISFcVe2oLa2trhX8HDx5EdHQ09uzZg9GjR6NJkyaoWbMmmjRpgjFjxmD37t24evWq2g8Kb9KkCQDgzJkzSuvCw8MBAG5ubmrtk4iIiEhXaTzP2M6dO9G5c2dYWlqqXG9lZYXOnTsrTHtRHM2bN4eNjQ327duH2NhY+fKsrCysWLECBgYG6NGjh3x5Wloa7ty5g7S0NM3eCBEREZGANL6b8vHjxzAyMnpvmTJlyqh956OBgQHmzZsHX19f9O/fH15eXhCLxQgLC8PDhw8xYcIE1KhRQ15+y5YtCAgIgJ+fH8aOHauwr+nTp8v/L3tg+dvLpk6dWuTzNYmIiIg+Jo2TscqVK+Po0aOYMGECypQpo7Q+OzsbR44c0egxRM2aNcPWrVuxfPlyHDx4UP6g8PHjx6Nbt27F3o+qmw7eXubn58dkjIiIiASlcTLWq1cvLFmyBP369cOYMWPQqFEjVKhQAc+fP8fly5fx119/ISkpCZMmTdJo/y4uLli7dm2R5caOHavUIiZz69YtjY5NREREVFI0TsZ8fX1x7949hIaGypMhPT095OfnAyiYs8jb2xu+vr7aiZSIiIjoE6RxMqanp4f58+eje/fu2LVrF27duoWsrCyIxWLUqVMH3bt3512PREREREX44Mchubm5MekiIiIi0pDGU1sQERER0YdjMkZEREQkICZjRERERAJiMkZEREQkICZjRERERAJiMkZEREQkICZjRERERAJiMkZEREQkICZjRERERAJiMkZEREQkICZjRERERAL64GdTElHpEG3epVjlEotRxiXtwIcFQ0REcmwZIyIiIhIQkzEiIiIiATEZIyIiIhIQkzEiIiIiATEZIyIiIhIQkzEiIiIiATEZIyIiIhIQkzEiIiIiAXHSVx3CSTmJiIg+P2wZIyIiIhIQkzEiIiIiATEZIyIiIhIQx4xRqcUxdkRE9ClgyxgRERGRgJiMEREREQmIyRgRERGRgJiMEREREQmIyRgRERGRgJiMEREREQmIyRgRERGRgJiMEREREQmIyRgRERGRgJiMEREREQmIyRgRERGRgJiMEREREQmIyRgRERGRgJiMEREREQmIyRgRERGRgJiMEREREQmIyRgRERGRgJiMEREREQmIyRgRERGRgJiMEREREQmIyRgRERGRgJiMEREREQmIyRgRERGRgJiMEREREQmIyRgRERGRgJiMEREREQlIZ5Ox6OhoDB8+HE2aNIGrqyt69eqFf/75R6195OfnY/PmzejatStcXFzQrFkzjB8/Hvfu3fs4QRMRERGpyUDoAFS5cOEChg0bBkNDQ3h6esLExARhYWGYPHkykpKSMGrUqGLt58cff8SOHTtQq1Yt/Oc//0FqaioOHDiA8PBwbN++HbVq1frI74SIiIjo/XQuGcvNzcXs2bMhEomwZcsW1KtXDwDw3XffoW/fvvD390enTp1gZ2f33v2cP38eO3bsQOPGjbF+/XoYGRkBALp3744hQ4bgp59+wubNmz/22yEiIiJ6L53rpjx//jwePHgALy8veSIGAGKxGGPGjEFubi5CQ0OL3E9wcDAAYMKECfJEDACaN28Od3d3XLx4EXfv3tX+GyAiIiJSg84lYxEREQAAd3d3pXUtW7ZUKPM+Fy5cgLGxMRo2bKi0TrbvixcvfkioRERERB9M57opZYPrbW1tldaZmpqiQoUKuH///nv38erVKzx9+hQODg7Q19dXWi/r4nzfQH6pVFrsmHVRaY+/pLG+1MP6Ug/rq/hYV+phfalHiPoSiURFltG5ZCwrKwsAYGJionK9WCzG48eP37uPFy9eyMsWto+3j6VKcSpP21zSDpT4MUsz1pd6WF/qYX0VH+tKPawv9XwO9aVz3ZREREREnxOdS8ZkrVay1q13ZWVlFdpqJiNbX1jLl2x5YS1nRERERCVF55Ix2XguVePCMjIy8Pz5c5Xjyd5mbGyMSpUq4eHDh8jLy1NaLxsrVtT0GEREREQfm84lY02aNAEAnDlzRmldeHg4AMDNza3I/bi5ueHVq1e4cuWK0jrZvmXHIiIiIhKKziVjzZs3h42NDfbt24fY2Fj58qysLKxYsQIGBgbo0aOHfHlaWhru3LmDtLQ0hf307t0bALBs2TLk5OTIl587dw5nzpxBkyZNUKNGjY/8boiIiIjeTyTVwftiz58/D19fXxgaGsLLywtisRhhYWF4+PAhJkyYgNGjR8vL+vv7IyAgAH5+fhg7dqzCfmbPno3g4GDUqlULbdq0kT8OqUyZMqXmcUh79uzB5cuXERMTg7i4OEgkEixYsADe3t5Ch6ZzUlJScPDgQZw6dQoJCQl49uwZTE1N0bBhQ/j6+qJBgwZCh6hTMjMzsXz5cly7dg0PHz5ERkYGKlSogBo1amDAgAHo0KGDIHcVlyZr1qzBH3/8AQAICgqCq6ursAHpmHbt2iEpKUnluj59+mDu3LklHFHpcOTIEWzduhU3btxAdnY2KlasCFdXV0yZMgVVqlQROjydEBoaihkzZry3TLNmzbBhw4YSiujD6NzUFkBBBW7duhXLly/HwYMHIZFIUKtWLYwfPx7dunUr9n7mzp0LR0dHBAUFYdOmTTA2Nkbbtm0xceLEUtMq9ueffyIpKQkVKlSApaVloV9sBGzatAlr1qxB9erV0aJFC1hYWOD+/fs4evQojh49isWLF6NLly5Ch6kznj9/jpCQEDRo0ADt27eHmZkZUlNT8e+//2LcuHHo3bs3fvnlF6HD1Fl37tzB8uXLYWxsjFevXgkdjs4yMTHBoEGDlJY7OTkJEI1uk0ql+PHHHxEUFITq1aujS5cuKFeuHJ48eYKLFy8iKSmJydj/q1u3Lvz8/FSuO3z4MG7fvq1y8nhdpZMtY/Q/Z8+eha2tLaytrREYGIjFixezZawQYWFhMDc3R+PGjRWWX7p0CYMHD0a5cuVw+vRphcdjfc7y8vIglUphYKD4N1lWVhb69OmD+Ph47Nu3D7Vr1xYoQt2Vl5eHPn36QCQSwc7ODnv37mXLmArt2rUDABw/flzgSEqHjRs34tdff8WAAQMwa9YspUnLc3Nzla5XUpSTk4NWrVohKysLJ0+eRMWKFYUOqVh0bswYKWrRogWsra2FDqNU6NChg1IiBgCNGzdG06ZNkZ6ejlu3bgkQmW7S19dX+cUuFovlf1EW9bSLz9WaNWtw8+ZNzJ8/X+VTPojU9fr1a/z111+wsbHBzJkzVX6umIgV7ejRo0hPT8dXX31VahIxQEe7KYm0TfYlxi+zor158wbnz5+HSCQqFeMqS1pcXBwCAgIwevRothoWQ05ODnbt2oWUlBSUL18eDRs2RJ06dYQOS+eEh4cjPT0dPXr0QH5+PsLCwnDv3j2YmJigRYsWRU7pRAV27twJAPj2228FjkQ9/GWiT15ycjLOnj2LSpUqwcHBQehwdE5mZiY2bNiA/Px8pKam4tSpU3j06BH8/Pw4F987cnNzMX36dNjb22PEiBFCh1MqPH36FNOnT1dY1qpVKyxatAjm5uYCRaV7YmJiABS0WHfr1g13796Vr9PT08PgwYMxbdo0ocIrFZKSknDu3DlYWVmhVatWQoejFiZj9EmTSCSYOnUqcnJyMHnyZHYpqZCZmYmAgAD5a0NDQ0ydOhVDhw4VMCrdtGrVKty6dQs7duyAoaGh0OHoPG9vb7i5uaFWrVowMjLCnTt3EBAQgFOnTmHMmDHYtm0b79j9f6mpqQCA9evXo169eggODoa9vT1iY2MxZ84crFu3DjY2Nujfv7/Akequ0NBQ5Ofnw9vbu9R913PMGH2y8vPzMXPmTFy8eBG9e/dG9+7dhQ5JJ1WrVg23bt3CjRs3cOzYMYwbNw5Lly7F2LFjkZubK3R4OuPmzZtYtWoVhg4divr16wsdTqng5+cHNzc3mJubQywWo0GDBli9ejUaNWqEyMhInDx5UugQdYbsXjpDQ0P89ddfcHFxQbly5dC4cWMsX74cenp6WL9+vcBR6q78/HyEhoZCJBKhZ8+eQoejNiZj9EmSSqWYPXs29u7di27duuHnn38WOiSdp6+vj2rVqmHEiBGYMGECjhw5gh07dggdls6YNm0abGxslOYzJPXo6enJ7wZX9YSUz5XsWclOTk6wsrJSWFe7dm3Y2NjgwYMHyMzMFCI8nRceHo7k5GQ0a9YMNjY2QoejNiZj9MmRtYiFhITAy8sLCxcuhJ4eP+rqkN1NGRERIXAkuuPmzZtISEiAs7MzHB0d5f927doFoGASU0dHRxw9elTgSHVfhQoVAADZ2dkCR6I7atasCaBgXjZVZMtfv35dYjGVJqV14L4Mx4zRJyU/Px+zZs1CaGgounTpgkWLFpW6sQO6ICUlBQBYd2/p1auXyuWXLl3CvXv30K5dO5ibm3MqmmKIjo4GANbVW5o2bQoASEhIUFonkUjw4MEDGBsb86YHFZ4/f45jx47BzMwMX3/9tdDhaITJGH0y3k7EOnXqhN9//53JxHvExsaiWrVqSn+Jp6enY+nSpQCA1q1bCxGaTvr1119VLp8+fTru3buHkSNHctLXt8THx8PS0hLly5dXWH7p0iWsX78eRkZG6NChg0DR6Z7q1avD3d0dZ86cQXBwsEILT2BgIDIzM9GtWzdOz6PCnj17IJFI0LVr11I7qTfPqo4LDg7G5cuXARTMbyRbJus+8vDwgIeHh2Dx6ZK//voLoaGhMDY2hp2dHVauXKlUxsPDA3Xr1hUgOt0TGhqKnTt3omnTpqhatSq++OILJCcn48SJE3j16hU6duyIrl27Ch0mlVIHDx7E2rVr0bx5c1hbW8PIyAhxcXEIDw+Hnp4efv75Z1StWlXoMHXKjz/+iL59+2L27Nk4evQoatasiRs3buD8+fOwtrbG1KlThQ5RJ4WEhAAovV2UAJMxnXf58mX5mBSZK1euyAe+WltbMxn7f7Lndr569QqrVq1SWcba2prJ2P/r2LEjsrKycPXqVVy8eBGvX7+GqakpGjVqhO7du8PT05PTDpDGmjZtijt37uDGjRuIiIhATk4OLCws0KVLFwwePBguLi5Ch6hzqlevjpCQECxfvhynT59GeHg4KlasiAEDBuC7776DhYWF0CHqnOjoaMTFxcHFxQWOjo5Ch6MxPpuSiIiISEC8xYyIiIhIQEzGiIiIiATEZIyIiIhIQEzGiIiIiATEZIyIiIhIQEzGiIiIiATEZIyIiIhIQEzGiIiIiATEZIyIiIhIQEzGiIiIiATEZIyIiIhIQEzGiIiIiATEZIyIiIhIQAZCB0BE9LYLFy7Ax8cHfn5+cHd3R0BAAKKiovDixQvcunULUqkUISEh2LlzJ+Li4pCXlwd7e3v0798fvXr1UtjXmzdvsGXLFuzevRtJSUnIz8+HhYUFXFxcMHLkSDg6OgIAQkNDMWPGDCxYsAAmJiZYtWoV4uPjIRaL4eHhgUmTJsHU1FQp1itXrmDVqlW4evUqsrOzYW1tDU9PT/j6+uKLL75QKOvo6Ag3Nzf8+eef+P3333HixAm8fPkSderUwffff4+mTZsqlH/y5AkCAwNx6tQpPH78GGXLloWVlRUaNWqEyZMnQywWy8vm5ORgy5Yt2Lt3L+7evQuRSIS6deti2LBhaN++vbZODRF9JCKpVCoVOggiIhlZMtayZUtERESgadOmcHR0xKNHj7BkyRJMnjwZ+/btg52dHZo2bQojIyOEh4cjISEBQ4cOxbRp0+T7mjBhAg4ePAhHR0d52UePHuHChQv4/vvv4e3tDeB/ydhXX32F8PBwdOrUCVWqVMHFixcRGRmJOnXqICgoCGXLlpXv+/Dhw5g0aRIMDQ3RuXNnWFhY4OzZs7h+/Tq+/PJLbNy4EUZGRvLyjo6OqFOnDl6/fo1y5cqhcePGSE1NxcGDB6Gnp4fQ0FA4ODgAALKzs+Hl5YWkpCS0bNkSjo6OkEgkSExMxLlz57B//35Uq1YNQEEiNmzYMERERKBevXpo1KgRJBIJTp48iUePHmHOnDn4z3/+UxKnjog0JSUi0iHnz5+XOjg4SB0cHKTBwcEK64KCgqQODg7SmTNnSiUSiXz5mzdvpCNHjpQ6ODhIr127JpVKpdLMzEypo6Oj1NvbW5qbm6uwn9zcXGlGRob8dUhIiPyYZ8+eVSg7ffp0qYODgzQgIEC+7MWLF9LGjRtLnZycpLGxsfLl+fn50kmTJkkdHBykf/31l8J+ZPv/6aefpHl5efLlO3bskDo4OEjnzJkjX3bs2DGpg4ODdP78+Ur18+LFC+mbN2/kr5csWSJ1cHCQ+vv7S/Pz8xXKeXt7S+vXry99/Pix0n6ISHdwzBgR6aR69eopdTtu3rwZxsbG+OGHH2Bg8L9RFkZGRpg4cSIAYP/+/QAAkUgEqVQKIyMj6OvrK+xHX18f5cuXVzpmy5Yt0bx5c4VlEyZMgKGhIXbv3i1fdvToUWRmZqJnz56oU6eOfLlIJMLkyZNhYGCAXbt2Ke3f2NgYkydPhp7e/756e/ToAQMDA8TExCiVf7slTkYsFstb3PLz87Ft2zbY2triu+++g0gkUij33XffQSKR4MiRI0r7ISLdwTFjRKSTnJ2dFV5nZ2cjLi4OlpaWCAwMVCqfm5sLAEhISABQkIy0atUKp0+fRo8ePdCxY0c0btwYLi4uCt2Hb2vUqJHSMisrK9jY2CAhIQFZWVkQi8WIjY0FALi5uSmVr1KlCmxsbHD37l15eRlbW1uUK1dOobyBgQEsLCyQmZkpX9akSRNUqlQJq1evRmxsLNq0aYNGjRrB0dFRIeG6e/cuMjIyYGlpiYCAAKVY0tLSFOqEiHQTkzEi0kkVK1ZUeJ2ZmQmpVIqUlBSViYfMq1ev5P9fvnw5Vq9ejX379mHp0qUAgHLlyqFnz56YNGmS0iB7CwuLQmNJSEjAy5cvIRaLkZWVpTJGmUqVKuHu3bvy8jImJiYqyxsYGCA/P1+h3Pbt2+Hv749///0XJ0+eBABUrlwZI0aMwIABAwAA6enpAIDbt2/j9u3bhdZJdnZ2oeuISHhMxohIJ73dAgRA3qJUv359hIaGFmsfxsbGmDhxIiZOnIjExERcuHAB27dvx8aNG/HmzRvMnTtXoXxqaqrK/Tx79kwhBlmCJVteVHlNVKtWDb/99hvy8vIQFxeHM2fOYNOmTZg7dy5MTU3h5eUlj6Njx45Yvny5xsciImFxzBgRlQpisRj29vZISEhQ6NIrLhsbG/Tq1Us+7uz48eNKZS5fvqy0LCUlBYmJiahevbo8+albty4AICIiotDyNjY2Cq1imtLX10fdunUxfPhwLFmyBADksdvb20MsFiMmJgYSieSDj0VEwmAyRkSlxsCBA5GdnY3Zs2crdEfKJCYm4uHDhwAKxktFR0crlcnIyIBEIkGZMmWU1oWHh+PcuXMKy5YtWwaJRILu3bvLl3l4eMDExAShoaEK3YNSqRSLFy+GRCJBjx49NH2biIuLQ1JSktJyWYubLHYDAwP069cPSUlJ+O2331QmZHFxcYW2+BGRbmA3JRGVGn379kVUVBR27dqFK1euoEWLFrC0tERqaioSEhIQFRWFxYsXo1q1akhJScG3336L2rVro169erCyskJ6ejqOHTsGiUQCX19fpf1/9dVXGD58uMp5xoYNGyYvJxaL8csvv+D7779H79690blzZ5ibm+PcuXOIiYmBi4uLyv0X19mzZ/Hbb7+hYcOGqFmzJszMzJCYmIjjx4+jbNmyCvOGjRs3Djdu3MCmTZtw8uRJNGnSBObm5khJSUFcXBxu3ryJoKCgQsfDEZHwmIwRUakhEomwcOFCtG7dGsHBwThx4gRevXoFc3Nz2NraYtq0afKpKaytrTF27FicP38eZ8+eRXp6OipUqIB69eph8ODBcHd3V9p/x44d0atXL6xcuRJhYWEQi8Xo06cPJk2apDTNROfOneV3PB45ckQ+A/+YMWMwfPhwlS1vxdWqVSskJSXh0qVLCAsLw6tXr2BlZSWf3d/e3l5e1sjICGvWrMHOnTuxe/duHD58GDk5OahYsSLs7e3Rt29f+WSyRKSbOAM/EX323n4ckmxWfiKiksIxY0REREQCYjJGREREJCAmY0REREQC4pgxIiIiIgGxZYyIiIhIQEzGiIiIiATEZIyIiIhIQEzGiIiIiATEZIyIiIhIQEzGiIiIiATEZIyIiIhIQEzGiIiIiAT0f8Bgby1jtU3KAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 600x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "run_direct_education_counterfactual(education_level=0, gender=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1350c47c",
   "metadata": {},
   "source": [
    "## Complex Causal Effects\n",
    "A few lessons here is that complex causal graphs seem like a lot work, but allow us to\n",
    "- map out an explicit generative model\n",
    "- map out an explicit estimand for a target causal effect -- we need to identify the correct adjustment set\n",
    "- generate simulations of nonlinear causal relationships and counterfactuals -- **DON'T DIRECTLY INTERPRET PARAMS, GENERATE PREDICTIONS**\n",
    "\n",
    "### Repeated Observations\n",
    "Note that some dimensions have repeat observations -- e.g. the story ID and the responder ID. We can leverage these repeat observations to estimate unobserved phenomena like individual response bias (similar to wine judge discrimination level), or story bias"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ffbd114c",
   "metadata": {},
   "source": [
    "# BONUS: Description and Causal Inference\n",
    "Mostly a review of previous studies, not much in terms of technical notes. The main point being that description (and prediction), which are generally  considered orthogonal to causal modeling, actually involve a causal model when performed correctly.\n",
    "\n",
    "## Things to look out for\n",
    "- Quality data > bigger data\n",
    "  - Bigger, biased data magnifies bias\n",
    "- Better models > averages (XBox poling). Better (causal) models can address unrepresentative samples\n",
    "- Post-stratification\n",
    "  - Still effects descriptive models\n",
    "  - NO CAUSES IN; NO DESCRIPTIONS OUT\n",
    "- Selection nodes\n",
    "  - can be incorporated into causal models to capture non-uniform participation\n",
    "- **The right action depends on the _causes_ of selection**\n",
    "- Always think carefully about potentially unmodeled selection bias\n",
    "\n",
    "## 4-step plan for honest digital scholarship\n",
    "1. Establish what we're trying to describe\n",
    "2. What is the ideal data for this description?\n",
    "3. What data do we actually have? This is almost never (2).\n",
    "4. What are **the causes** of the differences between (2) and (3)?\n",
    "5. (Optional) Can we use the data we actally have (3) and the model of what caused that data (4) to estimate what we're trying to describe (1)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9359a72a",
   "metadata": {},
   "source": [
    "## Authors\n",
    "* Ported to PyMC by Dustin Stansbury (2024)\n",
    "* Based on Statistical Rethinking (2023) lectures by Richard McElreath"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "1a6756c0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Last updated: Tue Dec 17 2024\n",
      "\n",
      "Python implementation: CPython\n",
      "Python version       : 3.12.5\n",
      "IPython version      : 8.27.0\n",
      "\n",
      "pytensor: 2.26.4\n",
      "aeppl   : not installed\n",
      "xarray  : 2024.7.0\n",
      "\n",
      "statsmodels: 0.14.2\n",
      "arviz      : 0.19.0\n",
      "pandas     : 2.2.2\n",
      "scipy      : 1.14.1\n",
      "pymc       : 5.19.1\n",
      "xarray     : 2024.7.0\n",
      "matplotlib : 3.9.2\n",
      "numpy      : 1.26.4\n",
      "\n",
      "Watermark: 2.5.0\n",
      "\n"
     ]
    }
   ],
   "source": [
    "%load_ext watermark\n",
    "%watermark -n -u -v -iv -w -p pytensor,aeppl,xarray"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6cb48c22",
   "metadata": {},
   "source": [
    ":::{include} ../page_footer.md\n",
    ":::"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}