Chapter_10.ipynb

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   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\aleks\\anaconda3\\envs\\causal_book_py38\\lib\\site-packages\\tqdm\\auto.py:22: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
      "  from .autonotebook import tqdm as notebook_tqdm\n"
     ]
    }
   ],
   "source": [
    "from copy import deepcopy\n",
    "import json\n",
    "import time\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from scipy import stats\n",
    "\n",
    "from sklearn.metrics import mean_absolute_percentage_error, accuracy_score, f1_score\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "import dowhy\n",
    "from dowhy import CausalModel\n",
    "\n",
    "from econml.metalearners import SLearner, XLearner, TLearner\n",
    "from econml.dml import LinearDML, CausalForestDML, DML\n",
    "from econml.dr import DRLearner, SparseLinearDRLearner\n",
    "\n",
    "from sklearn.linear_model import LinearRegression, LogisticRegression, LassoCV\n",
    "from sklearn.ensemble import RandomForestRegressor, GradientBoostingRegressor\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "from lightgbm import LGBMRegressor, LGBMClassifier\n",
    "\n",
    "import networkx as nx\n",
    "\n",
    "from tqdm import tqdm\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "plt.style.use('fivethirtyeight')\n",
    "\n",
    "import graphviz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'0.8'"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dowhy.__version__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "COLORS = [\n",
    "    '#00B0F0',\n",
    "    '#FF0000',\n",
    "    '#B0F000'\n",
    "]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Chapter 10\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Doubly Robust Methods: Let’s Get More!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_effect(effect_true, effect_pred, figsize=(10, 7), ylim=(5000, 22000)):\n",
    "    plt.figure(figsize=figsize)\n",
    "    plt.scatter(effect_true, effect_pred, color=COLORS[0])\n",
    "    plt.plot(np.sort(effect_true), np.sort(effect_true), color=COLORS[1], alpha=.7, label='Perfect model')\n",
    "    plt.xlabel('$True\\ effect$', fontsize=14, alpha=.5)\n",
    "    plt.ylabel('$Predicted\\ effect$', fontsize=14, alpha=.5)\n",
    "    plt.ylim(ylim[0], ylim[1])\n",
    "    plt.legend()\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Read the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Train set\n",
    "earnings_interaction_train = pd.read_csv(r'./data/ml_earnings_interaction_train.csv')\n",
    "\n",
    "# Test set\n",
    "earnings_interaction_test = pd.read_csv(r'./data/ml_earnings_interaction_test.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
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      "text/latex": [
       "$\\displaystyle \\left( \\left( 5000, \\  4\\right), \\  \\left( 100, \\  4\\right)\\right)$"
      ],
      "text/plain": [
       "((5000, 4), (100, 4))"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "earnings_interaction_train.shape, earnings_interaction_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>python_proficiency</th>\n",
       "      <th>took_a_course</th>\n",
       "      <th>earnings</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>23</td>\n",
       "      <td>0.632318</td>\n",
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       "      <td>139267.0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20</td>\n",
       "      <td>0.602551</td>\n",
       "      <td>False</td>\n",
       "      <td>115569.0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>21</td>\n",
       "      <td>0.518225</td>\n",
       "      <td>False</td>\n",
       "      <td>119142.0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>25</td>\n",
       "      <td>0.945161</td>\n",
       "      <td>False</td>\n",
       "      <td>130291.0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>30</td>\n",
       "      <td>0.636251</td>\n",
       "      <td>True</td>\n",
       "      <td>164209.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      "text/plain": [
       "   age  python_proficiency  took_a_course  earnings\n",
       "0   23            0.632318           True  139267.0\n",
       "1   20            0.602551          False  115569.0\n",
       "2   21            0.518225          False  119142.0\n",
       "3   25            0.945161          False  130291.0\n",
       "4   30            0.636251           True  164209.0"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Train \n",
    "earnings_interaction_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
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       "      <th>age</th>\n",
       "      <th>python_proficiency</th>\n",
       "      <th>took_a_course</th>\n",
       "      <th>true_effect</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>30</td>\n",
       "      <td>0.223877</td>\n",
       "      <td>True</td>\n",
       "      <td>11120.0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>23</td>\n",
       "      <td>0.394152</td>\n",
       "      <td>True</td>\n",
       "      <td>11970.0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>37</td>\n",
       "      <td>0.214638</td>\n",
       "      <td>True</td>\n",
       "      <td>11073.0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>21</td>\n",
       "      <td>0.869069</td>\n",
       "      <td>True</td>\n",
       "      <td>14345.0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>41</td>\n",
       "      <td>0.833934</td>\n",
       "      <td>True</td>\n",
       "      <td>14169.0</td>\n",
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       "   age  python_proficiency  took_a_course  true_effect\n",
       "0   30            0.223877           True      11120.0\n",
       "1   23            0.394152           True      11970.0\n",
       "2   37            0.214638           True      11073.0\n",
       "3   21            0.869069           True      14345.0\n",
       "4   41            0.833934           True      14169.0"
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     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Test\n",
    "earnings_interaction_test.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Define the graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Construct the graph (the graph is constant for all iterations)\n",
    "nodes = ['took_a_course', 'python_proficiency', 'earnings', 'age']\n",
    "edges = [\n",
    "    ('took_a_course', 'earnings'),\n",
    "    ('age', 'took_a_course'),\n",
    "    ('age', 'earnings'),\n",
    "    ('python_proficiency', 'earnings')\n",
    "]\n",
    "\n",
    "# Generate the GML graph\n",
    "gml_string = 'graph [directed 1\\n'\n",
    "\n",
    "for node in nodes:\n",
    "    gml_string += f'\\tnode [id \"{node}\" label \"{node}\"]\\n'\n",
    "\n",
    "for edge in edges:\n",
    "    gml_string += f'\\tedge [source \"{edge[0]}\" target \"{edge[1]}\"]\\n'\n",
    "    \n",
    "gml_string += ']'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Instantiate the CausalModel \n",
    "model = CausalModel(\n",
    "    data=earnings_interaction_train,\n",
    "    treatment='took_a_course',\n",
    "    outcome='earnings',\n",
    "    effect_modifiers='python_proficiency',\n",
    "    graph=gml_string\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model.view_model()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Get the estimand"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Estimand type: nonparametric-ate\n",
      "\n",
      "### Estimand : 1\n",
      "Estimand name: backdoor\n",
      "Estimand expression:\n",
      "       d                         \n",
      "────────────────(E[earnings|age])\n",
      "d[took_a_course]                 \n",
      "Estimand assumption 1, Unconfoundedness: If U→{took_a_course} and U→earnings then P(earnings|took_a_course,age,U) = P(earnings|took_a_course,age)\n",
      "\n",
      "### Estimand : 2\n",
      "Estimand name: iv\n",
      "No such variable(s) found!\n",
      "\n",
      "### Estimand : 3\n",
      "Estimand name: frontdoor\n",
      "No such variable(s) found!\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Get the estimand\n",
    "estimand = model.identify_effect()\n",
    "\n",
    "print(estimand)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Estimate the effect"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Function get_feature_names is deprecated; get_feature_names is deprecated in 1.0 and will be removed in 1.2. Please use get_feature_names_out instead.\n"
     ]
    }
   ],
   "source": [
    "# Get estimate (Doubly robust)\n",
    "estimate = model.estimate_effect(\n",
    "    identified_estimand=estimand,\n",
    "    method_name='backdoor.econml.dr.LinearDRLearner',\n",
    "    target_units='ate',\n",
    "    method_params={\n",
    "        'init_params': {\n",
    "            'model_propensity': LogisticRegression(),\n",
    "            'model_regression': LGBMRegressor(n_estimators=1000, max_depth=10)\n",
    "        },\n",
    "        'fit_params': {}\n",
    "    })"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/latex": [
       "$\\displaystyle 12586.9181504281$"
      ],
      "text/plain": [
       "12586.918150428053"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "estimate.cate_estimates.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compute predictions\n",
    "effect_pred = model.causal_estimator.effect(earnings_interaction_test.drop(['true_effect', 'took_a_course'], axis=1))\n",
    "\n",
    "# Get the true effect\n",
    "effect_true = earnings_interaction_test['true_effect'].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/latex": [
       "$\\displaystyle 0.00623739241153059$"
      ],
      "text/plain": [
       "0.006237392411530593"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Compute the error \n",
    "mean_absolute_percentage_error(effect_true, effect_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_effect(\n",
    "    effect_true=effect_true,\n",
    "    effect_pred=effect_pred,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Non-linear DR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Function get_feature_names is deprecated; get_feature_names is deprecated in 1.0 and will be removed in 1.2. Please use get_feature_names_out instead.\n"
     ]
    }
   ],
   "source": [
    "# Get estimate (Doubly robust)\n",
    "estimate = model.estimate_effect(\n",
    "    identified_estimand=estimand,\n",
    "    method_name='backdoor.econml.dr.DRLearner',\n",
    "    target_units='ate',\n",
    "    method_params={\n",
    "        'init_params': {\n",
    "            'model_propensity': LogisticRegression(),\n",
    "            'model_regression': LGBMRegressor(n_estimators=1000, max_depth=10),\n",
    "            'model_final': LGBMRegressor(n_estimators=500, max_depth=10),\n",
    "        },\n",
    "        'fit_params': {}\n",
    "    })"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compute predictions\n",
    "effect_pred = model.causal_estimator.effect(earnings_interaction_test.drop(['true_effect', 'took_a_course'], axis=1))\n",
    "\n",
    "# Get the true effect\n",
    "effect_true = earnings_interaction_test['true_effect'].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/latex": [
       "$\\displaystyle 0.0762854979135842$"
      ],
      "text/plain": [
       "0.07628549791358417"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Compute the error \n",
    "mean_absolute_percentage_error(effect_true, effect_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_effect(\n",
    "    effect_true=effect_true,\n",
    "    effect_pred=effect_pred,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## If Machine Learning is Cool, How About Double Machine Learning?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Estimate the effect"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Function get_feature_names is deprecated; get_feature_names is deprecated in 1.0 and will be removed in 1.2. Please use get_feature_names_out instead.\n"
     ]
    }
   ],
   "source": [
    "# Get estimate (DML)\n",
    "estimate = model.estimate_effect(\n",
    "    identified_estimand=estimand,\n",
    "    method_name='backdoor.econml.dml.LinearDML',\n",
    "    target_units='ate',\n",
    "    method_params={\n",
    "        'init_params': {\n",
    "            'model_y': LGBMRegressor(n_estimators=500, max_depth=10),\n",
    "            'model_t': LogisticRegression(),\n",
    "            'discrete_treatment': True\n",
    "        },\n",
    "        'fit_params': {}\n",
    "    })"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/latex": [
       "$\\displaystyle 12551.3789162371$"
      ],
      "text/plain": [
       "12551.378916237078"
      ]
     },
     "execution_count": 169,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "estimate.cate_estimates.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Predict on test data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compute predictions\n",
    "effect_pred = model.causal_estimator.effect(earnings_interaction_test.drop(['true_effect', 'took_a_course'], axis=1))\n",
    "\n",
    "# Get the true effect\n",
    "effect_true = earnings_interaction_test['true_effect'].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/latex": [
       "$\\displaystyle 0.0125345885989969$"
      ],
      "text/plain": [
       "0.012534588598996888"
      ]
     },
     "execution_count": 171,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Compute the error \n",
    "mean_absolute_percentage_error(effect_true, effect_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_effect(\n",
    "    effect_true=effect_true,\n",
    "    effect_pred=effect_pred,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### DML with more folds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Function get_feature_names is deprecated; get_feature_names is deprecated in 1.0 and will be removed in 1.2. Please use get_feature_names_out instead.\n"
     ]
    }
   ],
   "source": [
    "# Estimate the effect\n",
    "estimate = model.estimate_effect(\n",
    "    identified_estimand=estimand,\n",
    "    method_name='backdoor.econml.dml.LinearDML',\n",
    "    target_units='ate',\n",
    "    method_params={\n",
    "        'init_params': {\n",
    "            'model_y': LGBMRegressor(n_estimators=50, max_depth=10),\n",
    "            'model_t': LogisticRegression(),\n",
    "            'discrete_treatment': True,\n",
    "            'cv': 4\n",
    "        },\n",
    "        'fit_params': {\n",
    "        }\n",
    "    })"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/latex": [
       "$\\displaystyle 12615.3564587875$"
      ],
      "text/plain": [
       "12615.356458787523"
      ]
     },
     "execution_count": 190,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "estimate.cate_estimates.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compute predictions\n",
    "effect_pred = model.causal_estimator.effect(earnings_interaction_test.drop(['true_effect', 'took_a_course'], axis=1))\n",
    "\n",
    "# Get the true effect\n",
    "effect_true = earnings_interaction_test['true_effect'].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/latex": [
       "$\\displaystyle 0.00810075098627887$"
      ],
      "text/plain": [
       "0.008100750986278868"
      ]
     },
     "execution_count": 192,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Compute the error \n",
    "mean_absolute_percentage_error(effect_true, effect_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_effect(\n",
    "    effect_true=effect_true,\n",
    "    effect_pred=effect_pred,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### DML with cross-validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 268,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define wrapped CV models\n",
    "model_y = GridSearchCV(\n",
    "    estimator=LGBMRegressor(),\n",
    "    param_grid={\n",
    "        'max_depth': [3, 10, 20, 100],\n",
    "        'n_estimators': [10, 50, 100]\n",
    "    }, cv=10, n_jobs=-1, scoring='neg_mean_squared_error'\n",
    ")\n",
    "\n",
    "model_t = GridSearchCV(\n",
    "    estimator=LGBMClassifier(),\n",
    "    param_grid={\n",
    "        'max_depth': [3, 10, 20, 100],\n",
    "        'n_estimators': [10, 50, 100]\n",
    "    }, cv=10, n_jobs=-1, scoring='accuracy'\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 269,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Function get_feature_names is deprecated; get_feature_names is deprecated in 1.0 and will be removed in 1.2. Please use get_feature_names_out instead.\n"
     ]
    }
   ],
   "source": [
    "# Estimate the effect\n",
    "estimate = model.estimate_effect(\n",
    "    identified_estimand=estimand,\n",
    "    method_name='backdoor.econml.dml.LinearDML',\n",
    "    target_units='ate',\n",
    "    method_params={\n",
    "        'init_params': {\n",
    "            'model_y': model_y,\n",
    "            'model_t': model_t,\n",
    "            'discrete_treatment': True,\n",
    "            'cv': 4\n",
    "        },\n",
    "        'fit_params': {\n",
    "        }\n",
    "    })"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 270,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/latex": [
       "$\\displaystyle 12527.0883869255$"
      ],
      "text/plain": [
       "12527.088386925452"
      ]
     },
     "execution_count": 270,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "estimate.cate_estimates.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 271,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compute predictions\n",
    "effect_pred = model.causal_estimator.effect(earnings_interaction_test.drop(['true_effect', 'took_a_course'], axis=1))\n",
    "\n",
    "# Get the true effect\n",
    "effect_true = earnings_interaction_test['true_effect'].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 272,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/latex": [
       "$\\displaystyle 0.00179346825212699$"
      ],
      "text/plain": [
       "0.001793468252126986"
      ]
     },
     "execution_count": 272,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Compute the error \n",
    "mean_absolute_percentage_error(effect_true, effect_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 273,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_effect(\n",
    "    effect_true=effect_true,\n",
    "    effect_pred=effect_pred,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Causal Forests and more"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 400,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Function get_feature_names is deprecated; get_feature_names is deprecated in 1.0 and will be removed in 1.2. Please use get_feature_names_out instead.\n"
     ]
    }
   ],
   "source": [
    "# Estimate the effect\n",
    "estimate = model.estimate_effect(\n",
    "    identified_estimand=estimand,\n",
    "    method_name='backdoor.econml.dml.CausalForestDML',\n",
    "    target_units='ate',\n",
    "    method_params={\n",
    "        'init_params': {\n",
    "            'model_y': LGBMRegressor(n_estimators=50, max_depth=10),\n",
    "            'model_t': LGBMClassifier(n_estimators=50, max_depth=10),\n",
    "            'discrete_treatment': True,\n",
    "            'cv': 4\n",
    "        },\n",
    "        'fit_params': {\n",
    "        }\n",
    "    }\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 401,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/latex": [
       "$\\displaystyle 12467.1615864529$"
      ],
      "text/plain": [
       "12467.161586452929"
      ]
     },
     "execution_count": 401,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "estimate.cate_estimates.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 402,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compute predictions\n",
    "effect_pred = model.causal_estimator.effect(earnings_interaction_test.drop(['true_effect', 'took_a_course'], axis=1))\n",
    "\n",
    "# Get the true effect\n",
    "effect_true = earnings_interaction_test['true_effect'].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 403,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/latex": [
       "$\\displaystyle 0.0460233499035364$"
      ],
      "text/plain": [
       "0.04602334990353636"
      ]
     },
     "execution_count": 403,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Compute the error \n",
    "mean_absolute_percentage_error(effect_true, effect_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 404,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_effect(\n",
    "    effect_true=effect_true,\n",
    "    effect_pred=effect_pred,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Heterogenous Treatment Effects With Experimental Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Read in the data\n",
    "hillstrom_clean = pd.read_csv(r'./data/hillstrom_clean.csv')\n",
    "\n",
    "# Read in labels mapping\n",
    "with open(r'./data/hillstrom_clean_label_mapping.json', 'r') as f:\n",
    "    hillstrom_labels_mapping = json.load(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>recency</th>\n",
       "      <th>history</th>\n",
       "      <th>mens</th>\n",
       "      <th>womens</th>\n",
       "      <th>newbie</th>\n",
       "      <th>visit</th>\n",
       "      <th>conversion</th>\n",
       "      <th>spend</th>\n",
       "      <th>zip_code__rural</th>\n",
       "      <th>zip_code__surburban</th>\n",
       "      <th>zip_code__urban</th>\n",
       "      <th>channel__multichannel</th>\n",
       "      <th>channel__phone</th>\n",
       "      <th>channel__web</th>\n",
       "      <th>treatment</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10</td>\n",
       "      <td>142.44</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6</td>\n",
       "      <td>329.08</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7</td>\n",
       "      <td>180.65</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>9</td>\n",
       "      <td>675.83</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>45.34</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   recency  history  mens  womens  newbie  visit  conversion  spend  \\\n",
       "0       10   142.44     1       0       0      0           0    0.0   \n",
       "1        6   329.08     1       1       1      0           0    0.0   \n",
       "2        7   180.65     0       1       1      0           0    0.0   \n",
       "3        9   675.83     1       0       1      0           0    0.0   \n",
       "4        2    45.34     1       0       0      0           0    0.0   \n",
       "\n",
       "   zip_code__rural  zip_code__surburban  zip_code__urban  \\\n",
       "0                0                    1                0   \n",
       "1                1                    0                0   \n",
       "2                0                    1                0   \n",
       "3                1                    0                0   \n",
       "4                0                    0                1   \n",
       "\n",
       "   channel__multichannel  channel__phone  channel__web  treatment  \n",
       "0                      0               1             0          1  \n",
       "1                      0               0             1          0  \n",
       "2                      0               0             1          1  \n",
       "3                      0               0             1          2  \n",
       "4                      0               0             1          1  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hillstrom_clean.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Drop redundant cols to avoid multicollinearity\n",
    "hillstrom_clean = hillstrom_clean.drop(['zip_code__urban', 'channel__web'], axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### EDA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'control': 0, 'womans_email': 1, 'mens_email': 2}"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Display mapping\n",
    "hillstrom_labels_mapping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/latex": [
       "$\\displaystyle 0.14678125$"
      ],
      "text/plain": [
       "0.14678125"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# P(visit)\n",
    "hillstrom_clean.visit.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/latex": [
       "$\\displaystyle 0.00903125$"
      ],
      "text/plain": [
       "0.00903125"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# P(conversion)\n",
    "hillstrom_clean.conversion.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get sample size\n",
    "sample_size = hillstrom_clean.shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Check how random is the treatment assignment\n",
    "\n",
    "# Split data\n",
    "hillstrom_X = hillstrom_clean.drop(['visit', 'conversion', 'spend', 'treatment'], axis=1)\n",
    "hillstrom_Y = hillstrom_clean['spend']\n",
    "hillstrom_T = hillstrom_clean['treatment']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    0.334172\n",
       "2    0.332922\n",
       "0    0.332906\n",
       "Name: treatment, dtype: float64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# P(T=t)\n",
    "hillstrom_T.value_counts() / sample_size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Train-test split\n",
    "X_train_eda, X_test_eda, T_train_eda, T_test_eda = train_test_split(hillstrom_X, hillstrom_T, test_size=.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 348,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    0.335750\n",
       "0    0.334688\n",
       "2    0.329562\n",
       "Name: treatment, dtype: float64"
      ]
     },
     "execution_count": 348,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Split quality\n",
    "T_test_eda.value_counts() / T_test_eda.shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 322,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-3 {color: black;background-color: white;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LGBMClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LGBMClassifier</label><div class=\"sk-toggleable__content\"><pre>LGBMClassifier()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "LGBMClassifier()"
      ]
     },
     "execution_count": 322,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Fit the EDA model\n",
    "lgbm_eda = LGBMClassifier()\n",
    "lgbm_eda.fit(X_train_eda, T_train_eda)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 323,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/latex": [
       "$\\displaystyle 0.33384375$"
      ],
      "text/plain": [
       "0.33384375"
      ]
     },
     "execution_count": 323,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get predictions on the test\n",
    "T_pred_eda = lgbm_eda.predict(X_test_eda)\n",
    "\n",
    "# Check accuracy\n",
    "acc_eda = accuracy_score(T_test_eda, T_pred_eda)\n",
    "acc_eda"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 324,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/latex": [
       "$\\displaystyle \\left( 0.32831171875, \\  0.33859375\\right)$"
      ],
      "text/plain": [
       "(0.32831171875, 0.33859375)"
      ]
     },
     "execution_count": 324,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Generate random data\n",
    "random_scores = []\n",
    "\n",
    "test_eda_sample_size = T_test_eda.shape[0]\n",
    "\n",
    "for i in range(10000):\n",
    "    random_scores.append(\n",
    "        (np.random.choice(\n",
    "            [0, 1, 2], \n",
    "            test_eda_sample_size) == np.random.choice(\n",
    "            [0, 1, 2], \n",
    "            test_eda_sample_size)).mean())\n",
    "    \n",
    "np.quantile(random_scores, .025), np.quantile(random_scores, .975)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 325,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/latex": [
       "$\\displaystyle \\left( 0.32831171875, \\  0.33859375\\right)$"
      ],
      "text/plain": [
       "(0.32831171875, 0.33859375)"
      ]
     },
     "execution_count": 325,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get 95% CIs\n",
    "lower = np.quantile(random_scores, .025)\n",
    "upper = np.quantile(random_scores, .975)\n",
    "lower, upper"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 331,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot radom vs accuracy\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.fill_betweenx(\n",
    "    x1=lower, \n",
    "    x2=upper, \n",
    "    y=np.arange(0, 300),\n",
    "    color=COLORS[0],\n",
    "    alpha=.1,\n",
    "    label='95% empirical CI'\n",
    ")\n",
    "plt.hist(random_scores, alpha=.7, color=COLORS[0], bins=100, label='Random models\\n($n=10e3$)')\n",
    "plt.axvline(acc_eda, color=COLORS[1], ls='--', label='Model accuracy')\n",
    "\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8, 6))\n",
    "\n",
    "plt.hist(\n",
    "    hillstrom_clean[hillstrom_clean['treatment'] == 2]['spend'], \n",
    "    label=f'Men\\'s', \n",
    "    color=COLORS[0],\n",
    "    bins=100, \n",
    "    alpha=.5)\n",
    "\n",
    "plt.hist(\n",
    "    hillstrom_clean[hillstrom_clean['treatment'] == 1]['spend'], \n",
    "    label=f'Women\\'s',\n",
    "    color=COLORS[1],\n",
    "    bins=100, \n",
    "    alpha=.5)\n",
    "\n",
    "plt.legend()\n",
    "plt.yscale('log')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/latex": [
       "$\\displaystyle 228.496674834643$"
      ],
      "text/plain": [
       "228.49667483464316"
      ]
     },
     "execution_count": 152,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hillstrom_clean[hillstrom_clean['treatment'] == 1]['spend'].var()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'control': 0, 'womans_email': 1, 'mens_email': 2}"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hillstrom_labels_mapping"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Modeling Hillstrom"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 311,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "456"
      ]
     },
     "execution_count": 311,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# No. of conversions\n",
    "(hillstrom_Y[hillstrom_T > 0] > 0).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 821,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Train test split\n",
    "X_train, X_test, y_train, y_test, T_train, T_test = train_test_split(\n",
    "    hillstrom_X,\n",
    "    hillstrom_Y,\n",
    "    hillstrom_T,\n",
    "    test_size=.5\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 822,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(227, 229)"
      ]
     },
     "execution_count": 822,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# How many observations in train/test converted?\n",
    "(y_train[T_train > 0] > 0).sum(), (y_test[T_test > 0] > 0).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 823,
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_model(model_type, n_estimators=100, max_depth=10, learning_rate=.01):\n",
    "    if model_type == 'regressor':\n",
    "        return LGBMRegressor(\n",
    "            n_estimators=n_estimators, \n",
    "            max_depth=max_depth, \n",
    "            learning_rate=learning_rate)\n",
    "    elif model_type == 'classifier':\n",
    "        return LGBMClassifier(\n",
    "            n_estimators=n_estimators, \n",
    "            max_depth=max_depth, \n",
    "            learning_rate=learning_rate)\n",
    "    else:\n",
    "        raise NotImplementedError(f'Model type `{model_type}` not implemented.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 824,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Models\n",
    "s_learner = SLearner(\n",
    "    overall_model=create_model('regressor')\n",
    ")\n",
    "\n",
    "x_learner = XLearner(\n",
    "    models=[\n",
    "        create_model('regressor'),\n",
    "        create_model('regressor'),\n",
    "        create_model('regressor'),\n",
    "    ],\n",
    "    cate_models=[\n",
    "        create_model('regressor'),\n",
    "        create_model('regressor'),\n",
    "        create_model('regressor'),\n",
    "    ]\n",
    ")\n",
    "\n",
    "t_learner = TLearner(\n",
    "    models=[\n",
    "        create_model('regressor'),\n",
    "        create_model('regressor'),\n",
    "        create_model('regressor'),\n",
    "    ]\n",
    ")\n",
    "\n",
    "dml = LinearDML(\n",
    "    model_y=create_model('regressor'),\n",
    "    model_t=create_model('classifier'),\n",
    "    discrete_treatment=True,\n",
    "    cv=5\n",
    ")\n",
    "\n",
    "dr = DRLearner(\n",
    "    model_propensity=LogisticRegression(),\n",
    "    model_regression=create_model('regressor'),\n",
    "    model_final=create_model('regressor'),\n",
    "    cv=5,\n",
    ")\n",
    "\n",
    "cf = CausalForestDML(\n",
    "    model_y=create_model('regressor'),\n",
    "    model_t=create_model('classifier'),\n",
    "    discrete_treatment=True,\n",
    "    cv=5\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 825,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Models\n",
    "models = {\n",
    "    'SLearner': s_learner,\n",
    "    'TLearner': t_learner,\n",
    "    'XLearner': x_learner,\n",
    "    'DRLearner': dr,\n",
    "    'LinearDML': dml,\n",
    "    'CausalForestDML': cf\n",
    "} "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 826,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting SLearner\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Function get_feature_names is deprecated; get_feature_names is deprecated in 1.0 and will be removed in 1.2. Please use get_feature_names_out instead.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SLearner fitted in 0.2076 seconds.\n",
      "\n",
      "Fitting TLearner\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Function get_feature_names is deprecated; get_feature_names is deprecated in 1.0 and will be removed in 1.2. Please use get_feature_names_out instead.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TLearner fitted in 0.3739 seconds.\n",
      "\n",
      "Fitting XLearner\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Function get_feature_names is deprecated; get_feature_names is deprecated in 1.0 and will be removed in 1.2. Please use get_feature_names_out instead.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "XLearner fitted in 0.9840 seconds.\n",
      "\n",
      "Fitting DRLearner\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Function get_feature_names is deprecated; get_feature_names is deprecated in 1.0 and will be removed in 1.2. Please use get_feature_names_out instead.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DRLearner fitted in 2.5961 seconds.\n",
      "\n",
      "Fitting LinearDML\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Function get_feature_names is deprecated; get_feature_names is deprecated in 1.0 and will be removed in 1.2. Please use get_feature_names_out instead.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LinearDML fitted in 4.0818 seconds.\n",
      "\n",
      "Fitting CausalForestDML\n",
      "CausalForestDML fitted in 5.9465 seconds.\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Function get_feature_names is deprecated; get_feature_names is deprecated in 1.0 and will be removed in 1.2. Please use get_feature_names_out instead.\n"
     ]
    }
   ],
   "source": [
    "# Fit the estimator\n",
    "for model_name, model in models.items():\n",
    "    start = time.time()\n",
    "    print(f'Fitting {model_name}')\n",
    "    model.fit(\n",
    "        Y=y_train,\n",
    "        T=T_train,\n",
    "        X=X_train\n",
    "    )\n",
    "    stop = time.time()\n",
    "    \n",
    "    print(f'{model_name} fitted in {stop - start:0.4f} seconds.\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 827,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|████████████████████████████████████████████████████████████████████████████████████| 6/6 [00:01<00:00,  3.34it/s]\n",
      "100%|████████████████████████████████████████████████████████████████████████████████████| 6/6 [00:01<00:00,  3.56it/s]\n"
     ]
    }
   ],
   "source": [
    "# Compute effects\n",
    "effects_train = {\n",
    "    'treatment_1': {},\n",
    "    'treatment_2': {}\n",
    "}\n",
    "\n",
    "effects_test = {\n",
    "    'treatment_1': {},\n",
    "    'treatment_2': {}\n",
    "}\n",
    "\n",
    "\n",
    "for treatment in [1, 2]:\n",
    "    for model_name, model in tqdm(models.items()):\n",
    "        \n",
    "        # Compute effects on train\n",
    "        effects_local_train = models[model_name].effect(X_train.values, T0=0, T1=treatment)\n",
    "        effects_train[f'treatment_{treatment}'][model_name] = effects_local_train\n",
    "        \n",
    "        # Compute effects on test\n",
    "        effects_local_test = models[model_name].effect(X_test.values, T0=0, T1=treatment)\n",
    "        effects_test[f'treatment_{treatment}'][model_name] = effects_local_test\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Uplift by decile"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 828,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_uplift_by_decile(uplifts, t_true, t_pred, y_true):\n",
    "    \n",
    "    # Encapsulate the data & sort according to uplifts\n",
    "    all_data = pd.DataFrame(\n",
    "        dict(\n",
    "            uplifts=uplifts, \n",
    "            y_true=y_true, \n",
    "            t_true=t_true)\n",
    "    ).query(f't_true==0 | t_true=={t_pred}').sort_values('uplifts')\n",
    "    \n",
    "    # Partition into deciles\n",
    "    all_data['deciles'] = pd.qcut(all_data['uplifts'], q=10, labels=np.arange(10), duplicates='raise')\n",
    "    \n",
    "    # Get mean responses within deciles\n",
    "    mean_decile_resp = all_data.groupby(['deciles', 't_true']).mean()\n",
    "    \n",
    "    # Compute true decile uplift\n",
    "    mean_decile_resp['true_uplift'] = mean_decile_resp['y_true'] * np.array([-1, 1]*10)\n",
    "    true_uplift = mean_decile_resp.groupby(level=[0]).sum()['true_uplift']  \n",
    "    \n",
    "    return true_uplift[::-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 829,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2880x2160 with 24 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(40, 30))\n",
    "\n",
    "i = 1\n",
    "\n",
    "for model_name in models.keys():\n",
    "    \n",
    "    uplifts_by_decile = {\n",
    "        'treatment_1': {},\n",
    "        'treatment_2': {}\n",
    "    }\n",
    "    \n",
    "    global_min = np.inf\n",
    "    global_max = -np.inf\n",
    "    \n",
    "    for treatment in ['treatment_1', 'treatment_2']:\n",
    "\n",
    "        uplift_by_decile_train = get_uplift_by_decile(\n",
    "            uplifts=effects_train[treatment][model_name], \n",
    "            t_true=T_train,\n",
    "            t_pred=int(treatment.split('_')[-1]),\n",
    "            y_true=y_train\n",
    "        )\n",
    "\n",
    "        uplift_by_decile_test = get_uplift_by_decile(\n",
    "            uplifts=effects_test[treatment][model_name], \n",
    "            t_true=T_test,\n",
    "            t_pred=int(treatment.split('_')[-1]),\n",
    "            y_true=y_test\n",
    "        )\n",
    "            \n",
    "        plt.subplot(6, 4, i)\n",
    "        plt.bar(np.arange(10), uplift_by_decile_train, color=COLORS[0])\n",
    "        plt.title(f'{model_name} {treatment} - Train')\n",
    "        \n",
    "        plt.subplot(6, 4, i + 1)\n",
    "        plt.bar(np.arange(10), uplift_by_decile_test, color=COLORS[1])\n",
    "        plt.title(f'{model_name} {treatment} - Test')\n",
    "        \n",
    "        i += 2\n",
    "        \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Expected response\n",
    "\n",
    "As introduced by [Zhao et al., 2017](https://arxiv.org/pdf/1705.08492.pdf)\n",
    "\n",
    "Formula:\n",
    "\n",
    "$$\\Large Z = \\sum_{t=0}^K \\frac{1}{P(T=t)}y \\mathbb{I}_{h(x)=t}\\mathbb{I}_{T=t}$$\n",
    "\n",
    "<br>\n",
    "\n",
    "\n",
    "* $K$ is a number of treatment levels\n",
    "\n",
    "* $\\mathbb{I}$ is an indicator function\n",
    "\n",
    "* $h(x)$ is the treatment recommended by the model (treatment leading to the highest uplift)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 830,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_effects_argmax(effects_arrays, return_matrix=False):\n",
    "    \"\"\"Returns argmax for each row of predicted effects for the arbitrary no. of treatments.\n",
    "    \n",
    "    :param effects_arrays: A list of arrays for K treatments, where K>=1 (without control null effects)\n",
    "    :type effects_arrays: list of np.arrays\n",
    "    \n",
    "    :param return_matrix: Determines if the function returns a matrix of all effects \n",
    "        (with added null effect for control)\n",
    "    :type return_matrix: bool\n",
    "\n",
    "    ...\n",
    "    :return: A stacked matrix of all effects with added column for control effects (which is always 0)\n",
    "    :rtype: np.array\n",
    "    \"\"\"\n",
    "    \n",
    "    n_rows = effects_arrays[0].shape[0]\n",
    "    null_effect_array = np.zeros(n_rows)\n",
    "    stacked = np.stack([null_effect_array] + effects_arrays).T\n",
    "    \n",
    "    if return_matrix:\n",
    "        return np.argmax(stacked, axis=1), stacked\n",
    "    \n",
    "    return np.argmax(stacked, axis=1)\n",
    "\n",
    "\n",
    "def get_expected_response(y_true, t_true, effects_argmax):\n",
    "    \"\"\"Computes the average expected response for an uplift model according to the formula\n",
    "        proposed by: \n",
    "        Zhao, Y., Fang, X., & Simchi-Levi, D. (2017). Uplift Modeling with Multiple Treatments and General Response Types. \n",
    "        Proceedings of the 2017 SIAM International Conference on Data Mining, 588-596. \n",
    "        Society for Industrial and Applied Mathematics.   \n",
    "    \"\"\"\n",
    "    \n",
    "    proba_t = pd.Series(t_true).value_counts() / np.array(t_true).shape[0]\n",
    "    treatments = proba_t.index.values\n",
    "    \n",
    "    z_vals = 0\n",
    "    \n",
    "    for treatment in treatments:\n",
    "        h_indicator = effects_argmax == treatment\n",
    "        t_indicator = t_true == treatment\n",
    "        t_proba_local = proba_t[treatment]\n",
    "        \n",
    "        z_vals += (1/t_proba_local) * y_true * h_indicator * t_indicator\n",
    "    \n",
    "    return z_vals.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 833,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Expecetd response on train:\n",
      "\n",
      "SLearner: 2.004509358884219\n",
      "TLearner: 2.499033323345889\n",
      "XLearner: 2.3844783394170035\n",
      "DRLearner: 2.357949913732327\n",
      "LinearDML: 1.5596898804820036\n",
      "CausalForestDML: 2.9583483277085936\n",
      "\n",
      "------------------------------\n",
      "Expected response on test:\n",
      "\n",
      "SLearner: 1.3498049695400602\n",
      "TLearner: 1.2108070097957537\n",
      "XLearner: 1.2901946274863276\n",
      "DRLearner: 1.500974856751045\n",
      "LinearDML: 1.3496321556492639\n",
      "CausalForestDML: 1.3114730998145985\n"
     ]
    }
   ],
   "source": [
    "# Compute expected response\n",
    "print('Expecetd response on train:\\n')\n",
    "for model_name in models:\n",
    "    effects_argmax = get_effects_argmax(\n",
    "        [\n",
    "            effects_train['treatment_1'][model_name],\n",
    "            effects_train['treatment_2'][model_name]\n",
    "        ]\n",
    "    )\n",
    "    \n",
    "    expected_response = get_expected_response(\n",
    "        y_true=y_train, \n",
    "        t_true=T_train, \n",
    "        effects_argmax=effects_argmax\n",
    "    )\n",
    "    \n",
    "    print(f'{model_name}: {expected_response}')\n",
    "    \n",
    "print('\\n' + '-'*30)\n",
    "    \n",
    "print('Expected response on test:\\n')\n",
    "for model_name in models:\n",
    "    effects_argmax = get_effects_argmax(\n",
    "        [\n",
    "            effects_test['treatment_1'][model_name],\n",
    "            effects_test['treatment_2'][model_name]\n",
    "        ]\n",
    "    )\n",
    "    \n",
    "    expected_response = get_expected_response(\n",
    "        y_true=y_test, \n",
    "        t_true=T_test, \n",
    "        effects_argmax=effects_argmax\n",
    "    )\n",
    "    \n",
    "    print(f'{model_name}: {expected_response}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 834,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "treatment\n",
       "0    0.652789\n",
       "1    1.077202\n",
       "2    1.422617\n",
       "Name: spend, dtype: float64"
      ]
     },
     "execution_count": 834,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Outcome in the whole dataset\n",
    "hillstrom_clean.groupby('treatment')['spend'].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Confidence intervals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 835,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([-1.41417908, -0.40901173, -1.50891344, ..., -0.27946152,\n",
       "        -0.8185368 , -0.92689394]),\n",
       " array([0.88022207, 1.03706657, 3.34888932, ..., 2.47925003, 1.75716167,\n",
       "        1.51788005]))"
      ]
     },
     "execution_count": 835,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "models['LinearDML'].effect_interval(X=X_test, T0=0, T1=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 844,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([-1.41417908]), array([0.88022207]))"
      ]
     },
     "execution_count": 844,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "models['LinearDML'].effect_interval(X=X_test.iloc[0:1, :], T0=0, T1=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 836,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/latex": [
       "$\\displaystyle 0.100875$"
      ],
      "text/plain": [
       "0.100875"
      ]
     },
     "execution_count": 836,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# CIs (DML)\n",
    "ints = np.stack(models['LinearDML'].effect_interval(X=X_test, T0=0, T1=1, alpha=.05)).T\n",
    "\n",
    "# What % of effects contains zero?\n",
    "(np.sign(ints[:, 0]) == np.sign(ints[:, 1])).sum() / ints.shape[0]"
   ]
  }
 ],
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