{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-14T18:45:14.446326Z",
     "start_time": "2020-04-14T18:45:14.429178Z"
    }
   },
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-14T18:45:20.337967Z",
     "start_time": "2020-04-14T18:45:14.448942Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/jeong/.conda/envs/py36/lib/python3.6/site-packages/sklearn/utils/deprecation.py:144: FutureWarning: The sklearn.utils.testing module is  deprecated in version 0.22 and will be removed in version 0.24. The corresponding classes / functions should instead be imported from sklearn.utils. Anything that cannot be imported from sklearn.utils is now part of the private API.\n",
      "  warnings.warn(message, FutureWarning)\n",
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from matplotlib import pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.model_selection import train_test_split, StratifiedKFold\n",
    "from sklearn.linear_model import LogisticRegressionCV, LogisticRegression\n",
    "from xgboost import XGBRegressor\n",
    "from lightgbm import LGBMRegressor\n",
    "from sklearn.metrics import mean_absolute_error\n",
    "from sklearn.metrics import mean_squared_error as mse\n",
    "from scipy.stats import entropy\n",
    "import warnings\n",
    "\n",
    "from causalml.inference.meta import LRSRegressor\n",
    "from causalml.inference.meta import XGBTRegressor, MLPTRegressor\n",
    "from causalml.inference.meta import BaseXRegressor, BaseRRegressor, BaseSRegressor, BaseTRegressor\n",
    "from causalml.inference.nn import DragonNet\n",
    "from causalml.match import NearestNeighborMatch, MatchOptimizer, create_table_one\n",
    "from causalml.propensity import ElasticNetPropensityModel\n",
    "from causalml.dataset.regression import *\n",
    "from causalml.metrics import *\n",
    "\n",
    "import os, sys\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "warnings.filterwarnings('ignore')\n",
    "plt.style.use('fivethirtyeight')\n",
    "sns.set_palette('Paired')\n",
    "plt.rcParams['figure.figsize'] = (12,8)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# IHDP semi-synthetic dataset\n",
    "\n",
    "Hill introduced a semi-synthetic dataset constructed from the Infant Health\n",
    "and Development Program (IHDP). This dataset is based on a randomized experiment\n",
    "investigating the effect of home visits by specialists on future cognitive scores. The data has 747 observations (rows). The IHDP simulation is considered the de-facto standard benchmark for neural network treatment effect\n",
    "estimation methods.\n",
    "\n",
    "The original [paper](https://arxiv.org/pdf/1906.02120.pdf) uses 1000 realizations from the NCPI package, but for illustration purposes, we use 1 dataset (realization) as an example below. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-14T18:45:20.490305Z",
     "start_time": "2020-04-14T18:45:20.347683Z"
    }
   },
   "outputs": [],
   "source": [
    "df = pd.read_csv(f'data/ihdp_npci_3.csv', header=None)\n",
    "cols =  [\"treatment\", \"y_factual\", \"y_cfactual\", \"mu0\", \"mu1\"] + [f'x{i}' for i in range(1,26)]\n",
    "df.columns = cols"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-14T18:45:20.604760Z",
     "start_time": "2020-04-14T18:45:20.494854Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(747, 30)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-14T18:45:20.750421Z",
     "start_time": "2020-04-14T18:45:20.607948Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "<style scoped>\n",
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       "    .dataframe tbody tr th {\n",
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       "    }\n",
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       "    .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>treatment</th>\n",
       "      <th>y_factual</th>\n",
       "      <th>y_cfactual</th>\n",
       "      <th>mu0</th>\n",
       "      <th>mu1</th>\n",
       "      <th>x1</th>\n",
       "      <th>x2</th>\n",
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      "text/plain": [
       "   treatment  y_factual  y_cfactual       mu0       mu1        x1        x2  \\\n",
       "0          1   5.931652    3.500591  2.253801  7.136441 -0.528603 -0.343455   \n",
       "1          0   2.175966    5.952101  1.257592  6.553022 -1.736945 -1.802002   \n",
       "2          0   2.180294    7.175734  2.384100  7.192645 -0.807451 -0.202946   \n",
       "3          0   3.587662    7.787537  4.009365  7.712456  0.390083  0.596582   \n",
       "4          0   2.372618    5.461871  2.481631  7.232739 -1.045229 -0.602710   \n",
       "\n",
       "         x3        x4        x5  ...  x16  x17  x18  x19  x20  x21  x22  x23  \\\n",
       "0  1.128554  0.161703 -0.316603  ...    1    1    1    1    0    0    0    0   \n",
       "1  0.383828  2.244320 -0.629189  ...    1    1    1    1    0    0    0    0   \n",
       "2 -0.360898 -0.879606  0.808706  ...    1    0    1    1    0    0    0    0   \n",
       "3 -1.850350 -0.879606 -0.004017  ...    1    0    1    1    0    0    0    0   \n",
       "4  0.011465  0.161703  0.683672  ...    1    1    1    1    0    0    0    0   \n",
       "\n",
       "   x24  x25  \n",
       "0    0    0  \n",
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       "3    0    0  \n",
       "4    0    0  \n",
       "\n",
       "[5 rows x 30 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-14T18:45:20.862928Z",
     "start_time": "2020-04-14T18:45:20.753591Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0.813922\n",
       "1    0.186078\n",
       "Name: treatment, dtype: float64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.Series(df['treatment']).value_counts(normalize=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-14T18:45:21.024279Z",
     "start_time": "2020-04-14T18:45:20.869438Z"
    }
   },
   "outputs": [],
   "source": [
    "X = df.loc[:,'x1':]\n",
    "treatment = df['treatment']\n",
    "y = df['y_factual']\n",
    "tau = df.apply(lambda d: d['y_factual'] - d['y_cfactual'] if d['treatment']==1 \n",
    "               else d['y_cfactual'] - d['y_factual'], \n",
    "               axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-14T18:45:21.139835Z",
     "start_time": "2020-04-14T18:45:21.031386Z"
    }
   },
   "outputs": [],
   "source": [
    "# p_model = LogisticRegressionCV(penalty='elasticnet', solver='saga', l1_ratios=np.linspace(0,1,5),\n",
    "#                                cv=StratifiedKFold(n_splits=4, shuffle=True))\n",
    "# p_model.fit(X, treatment)\n",
    "# p = p_model.predict_proba(X)[:, 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-14T18:45:22.519115Z",
     "start_time": "2020-04-14T18:45:21.143286Z"
    }
   },
   "outputs": [],
   "source": [
    "p_model = ElasticNetPropensityModel()\n",
    "p = p_model.fit_predict(X, treatment)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-14T18:45:37.162341Z",
     "start_time": "2020-04-14T18:45:22.521550Z"
    }
   },
   "outputs": [],
   "source": [
    "s_learner = BaseSRegressor(LGBMRegressor())\n",
    "s_ate = s_learner.estimate_ate(X, treatment, y)[0]\n",
    "s_ite = s_learner.fit_predict(X, treatment, y)\n",
    "\n",
    "t_learner = BaseTRegressor(LGBMRegressor())\n",
    "t_ate = t_learner.estimate_ate(X, treatment, y)[0][0]\n",
    "t_ite = t_learner.fit_predict(X, treatment, y)\n",
    "\n",
    "x_learner = BaseXRegressor(LGBMRegressor())\n",
    "x_ate = x_learner.estimate_ate(X, treatment, y, p)[0][0]\n",
    "x_ite = x_learner.fit_predict(X, treatment, y, p)\n",
    "\n",
    "r_learner = BaseRRegressor(LGBMRegressor())\n",
    "r_ate = r_learner.estimate_ate(X, treatment, y, p)[0][0]\n",
    "r_ite = r_learner.fit_predict(X, treatment, y, p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-14T18:45:45.332645Z",
     "start_time": "2020-04-14T18:45:37.167054Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 597 samples, validate on 150 samples\n",
      "Epoch 1/30\n",
      "597/597 [==============================] - 1s 1ms/step - loss: 1153.1169 - regression_loss: 526.5245 - binary_classification_loss: 34.2278 - treatment_accuracy: 0.7999 - track_epsilon: 0.0516 - val_loss: 356.0019 - val_regression_loss: 126.8068 - val_binary_classification_loss: 34.7623 - val_treatment_accuracy: 0.7244 - val_track_epsilon: 0.0513\n",
      "Epoch 2/30\n",
      "597/597 [==============================] - 0s 67us/step - loss: 343.8514 - regression_loss: 142.3123 - binary_classification_loss: 28.2888 - treatment_accuracy: 0.8434 - track_epsilon: 0.0513 - val_loss: 230.0812 - val_regression_loss: 81.2849 - val_binary_classification_loss: 34.9740 - val_treatment_accuracy: 0.7244 - val_track_epsilon: 0.0496\n",
      "Epoch 3/30\n",
      "597/597 [==============================] - 0s 73us/step - loss: 255.0366 - regression_loss: 108.9301 - binary_classification_loss: 26.8012 - treatment_accuracy: 0.8465 - track_epsilon: 0.0490 - val_loss: 235.1863 - val_regression_loss: 82.9400 - val_binary_classification_loss: 35.9143 - val_treatment_accuracy: 0.7244 - val_track_epsilon: 0.0493\n",
      "Epoch 4/30\n",
      "597/597 [==============================] - 0s 83us/step - loss: 214.3295 - regression_loss: 84.8636 - binary_classification_loss: 26.3836 - treatment_accuracy: 0.8561 - track_epsilon: 0.0496 - val_loss: 206.7090 - val_regression_loss: 66.4528 - val_binary_classification_loss: 36.8853 - val_treatment_accuracy: 0.7244 - val_track_epsilon: 0.0489\n",
      "Epoch 5/30\n",
      "597/597 [==============================] - 0s 63us/step - loss: 193.1023 - regression_loss: 77.6289 - binary_classification_loss: 25.8865 - treatment_accuracy: 0.8497 - track_epsilon: 0.0478 - val_loss: 204.8226 - val_regression_loss: 71.0998 - val_binary_classification_loss: 35.7694 - val_treatment_accuracy: 0.7244 - val_track_epsilon: 0.0470\n",
      "Epoch 6/30\n",
      "597/597 [==============================] - 0s 62us/step - loss: 181.7809 - regression_loss: 71.4368 - binary_classification_loss: 25.3941 - treatment_accuracy: 0.8593 - track_epsilon: 0.0469 - val_loss: 209.0668 - val_regression_loss: 68.9204 - val_binary_classification_loss: 36.2566 - val_treatment_accuracy: 0.7244 - val_track_epsilon: 0.0464\n",
      "Epoch 7/30\n",
      "597/597 [==============================] - 0s 61us/step - loss: 176.5884 - regression_loss: 70.6928 - binary_classification_loss: 25.1117 - treatment_accuracy: 0.8561 - track_epsilon: 0.0455 - val_loss: 203.3805 - val_regression_loss: 69.1391 - val_binary_classification_loss: 35.8173 - val_treatment_accuracy: 0.7244 - val_track_epsilon: 0.0446\n",
      "Epoch 8/30\n",
      "597/597 [==============================] - 0s 65us/step - loss: 170.7210 - regression_loss: 66.4146 - binary_classification_loss: 24.8363 - treatment_accuracy: 0.8401 - track_epsilon: 0.0441 - val_loss: 192.5185 - val_regression_loss: 62.5455 - val_binary_classification_loss: 36.8282 - val_treatment_accuracy: 0.7244 - val_track_epsilon: 0.0433\n",
      "Epoch 9/30\n",
      "597/597 [==============================] - 0s 66us/step - loss: 160.6429 - regression_loss: 61.6206 - binary_classification_loss: 24.6174 - treatment_accuracy: 0.8497 - track_epsilon: 0.0426 - val_loss: 194.9871 - val_regression_loss: 64.1374 - val_binary_classification_loss: 36.2175 - val_treatment_accuracy: 0.7244 - val_track_epsilon: 0.0418\n",
      "Epoch 10/30\n",
      "597/597 [==============================] - 0s 65us/step - loss: 160.4497 - regression_loss: 61.8506 - binary_classification_loss: 24.4592 - treatment_accuracy: 0.8497 - track_epsilon: 0.0412 - val_loss: 188.0958 - val_regression_loss: 60.7865 - val_binary_classification_loss: 36.4476 - val_treatment_accuracy: 0.7244 - val_track_epsilon: 0.0403\n",
      "Epoch 11/30\n",
      "597/597 [==============================] - 0s 62us/step - loss: 159.8468 - regression_loss: 62.8502 - binary_classification_loss: 24.3127 - treatment_accuracy: 0.8529 - track_epsilon: 0.0395 - val_loss: 197.3698 - val_regression_loss: 63.0735 - val_binary_classification_loss: 36.6958 - val_treatment_accuracy: 0.7244 - val_track_epsilon: 0.0390\n",
      "Epoch 12/30\n",
      "597/597 [==============================] - 0s 57us/step - loss: 159.8472 - regression_loss: 61.2275 - binary_classification_loss: 24.2195 - treatment_accuracy: 0.8497 - track_epsilon: 0.0383 - val_loss: 190.8406 - val_regression_loss: 64.3669 - val_binary_classification_loss: 35.1488 - val_treatment_accuracy: 0.7244 - val_track_epsilon: 0.0372\n",
      "Train on 597 samples, validate on 150 samples\n",
      "Epoch 1/300\n",
      "597/597 [==============================] - 1s 1ms/step - loss: 151.0525 - regression_loss: 58.8814 - binary_classification_loss: 24.1191 - treatment_accuracy: 0.8529 - track_epsilon: 0.0377 - val_loss: 184.5767 - val_regression_loss: 59.0096 - val_binary_classification_loss: 35.9360 - val_treatment_accuracy: 0.7244 - val_track_epsilon: 0.0390\n",
      "Epoch 2/300\n",
      "597/597 [==============================] - 0s 67us/step - loss: 150.0762 - regression_loss: 56.8447 - binary_classification_loss: 24.1029 - treatment_accuracy: 0.8497 - track_epsilon: 0.0326 - val_loss: 184.1211 - val_regression_loss: 59.6037 - val_binary_classification_loss: 35.7386 - val_treatment_accuracy: 0.7244 - val_track_epsilon: 0.0227\n",
      "Epoch 3/300\n",
      "597/597 [==============================] - 0s 67us/step - loss: 149.7746 - regression_loss: 57.0947 - binary_classification_loss: 24.0785 - treatment_accuracy: 0.8561 - track_epsilon: 0.0181 - val_loss: 181.9517 - val_regression_loss: 59.1016 - val_binary_classification_loss: 35.8648 - val_treatment_accuracy: 0.7244 - val_track_epsilon: 0.0142\n",
      "Epoch 4/300\n",
      "597/597 [==============================] - 0s 68us/step - loss: 148.7084 - regression_loss: 57.0758 - binary_classification_loss: 24.0558 - treatment_accuracy: 0.8561 - track_epsilon: 0.0127 - val_loss: 182.8566 - val_regression_loss: 59.1128 - val_binary_classification_loss: 35.9316 - val_treatment_accuracy: 0.7244 - val_track_epsilon: 0.0076\n",
      "Epoch 5/300\n",
      "597/597 [==============================] - 0s 69us/step - loss: 150.4725 - regression_loss: 56.8933 - binary_classification_loss: 24.0455 - treatment_accuracy: 0.8529 - track_epsilon: 0.0040 - val_loss: 182.9057 - val_regression_loss: 59.1165 - val_binary_classification_loss: 36.0808 - val_treatment_accuracy: 0.7244 - val_track_epsilon: 0.0022\n",
      "Epoch 6/300\n",
      "597/597 [==============================] - 0s 68us/step - loss: 147.7774 - regression_loss: 56.4606 - binary_classification_loss: 24.0391 - treatment_accuracy: 0.8593 - track_epsilon: 0.0013 - val_loss: 183.9675 - val_regression_loss: 59.4084 - val_binary_classification_loss: 36.1876 - val_treatment_accuracy: 0.7244 - val_track_epsilon: 0.0024\n",
      "Epoch 7/300\n",
      "597/597 [==============================] - 0s 67us/step - loss: 149.8826 - regression_loss: 57.2671 - binary_classification_loss: 24.0319 - treatment_accuracy: 0.8529 - track_epsilon: 0.0028 - val_loss: 186.5590 - val_regression_loss: 60.4098 - val_binary_classification_loss: 36.1753 - val_treatment_accuracy: 0.7244 - val_track_epsilon: 4.0377e-04\n",
      "Epoch 8/300\n",
      "597/597 [==============================] - 0s 70us/step - loss: 148.1314 - regression_loss: 56.3730 - binary_classification_loss: 24.0128 - treatment_accuracy: 0.8561 - track_epsilon: 0.0021 - val_loss: 183.1079 - val_regression_loss: 59.5408 - val_binary_classification_loss: 36.1076 - val_treatment_accuracy: 0.7244 - val_track_epsilon: 0.0031\n",
      "Epoch 9/300\n",
      "597/597 [==============================] - 0s 70us/step - loss: 148.6218 - regression_loss: 56.6761 - binary_classification_loss: 23.9945 - treatment_accuracy: 0.8561 - track_epsilon: 0.0017 - val_loss: 183.6684 - val_regression_loss: 59.4958 - val_binary_classification_loss: 36.1848 - val_treatment_accuracy: 0.7244 - val_track_epsilon: 0.0041\n",
      "Epoch 10/300\n",
      "597/597 [==============================] - 0s 80us/step - loss: 147.2199 - regression_loss: 55.6598 - binary_classification_loss: 23.9914 - treatment_accuracy: 0.8561 - track_epsilon: 0.0037 - val_loss: 187.5044 - val_regression_loss: 60.6762 - val_binary_classification_loss: 36.0621 - val_treatment_accuracy: 0.7244 - val_track_epsilon: 0.0018\n",
      "Epoch 11/300\n",
      "597/597 [==============================] - ETA: 0s - loss: 184.3614 - regression_loss: 74.6064 - binary_classification_loss: 30.5212 - treatment_accuracy: 0.8281 - track_epsilon: 0.001 - 0s 67us/step - loss: 149.2038 - regression_loss: 56.6065 - binary_classification_loss: 23.9720 - treatment_accuracy: 0.8465 - track_epsilon: 0.0028 - val_loss: 185.2099 - val_regression_loss: 59.7681 - val_binary_classification_loss: 36.0292 - val_treatment_accuracy: 0.7244 - val_track_epsilon: 1.1392e-04\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 12/300\n",
      "597/597 [==============================] - 0s 65us/step - loss: 144.7243 - regression_loss: 56.0806 - binary_classification_loss: 23.9684 - treatment_accuracy: 0.8401 - track_epsilon: 0.0012 - val_loss: 182.7289 - val_regression_loss: 59.1949 - val_binary_classification_loss: 36.1361 - val_treatment_accuracy: 0.7244 - val_track_epsilon: 0.0018\n",
      "Epoch 13/300\n",
      "597/597 [==============================] - 0s 82us/step - loss: 146.8869 - regression_loss: 56.1869 - binary_classification_loss: 23.9454 - treatment_accuracy: 0.8593 - track_epsilon: 0.0012 - val_loss: 181.4378 - val_regression_loss: 58.5800 - val_binary_classification_loss: 36.0047 - val_treatment_accuracy: 0.7244 - val_track_epsilon: 0.0020\n",
      "Epoch 14/300\n",
      "597/597 [==============================] - 0s 64us/step - loss: 145.5166 - regression_loss: 55.1947 - binary_classification_loss: 23.9264 - treatment_accuracy: 0.8497 - track_epsilon: 0.0028 - val_loss: 183.9117 - val_regression_loss: 59.8171 - val_binary_classification_loss: 36.0495 - val_treatment_accuracy: 0.7244 - val_track_epsilon: 0.0023\n",
      "Epoch 15/300\n",
      "597/597 [==============================] - 0s 67us/step - loss: 147.9824 - regression_loss: 55.9960 - binary_classification_loss: 23.9193 - treatment_accuracy: 0.8561 - track_epsilon: 0.0013 - val_loss: 184.8934 - val_regression_loss: 60.1771 - val_binary_classification_loss: 36.0228 - val_treatment_accuracy: 0.7244 - val_track_epsilon: 0.0027\n",
      "Epoch 16/300\n",
      "597/597 [==============================] - 0s 67us/step - loss: 146.7458 - regression_loss: 55.8055 - binary_classification_loss: 23.8981 - treatment_accuracy: 0.8561 - track_epsilon: 0.0022 - val_loss: 184.1797 - val_regression_loss: 59.5255 - val_binary_classification_loss: 35.9737 - val_treatment_accuracy: 0.7244 - val_track_epsilon: 9.6159e-04\n",
      "Epoch 17/300\n",
      "597/597 [==============================] - 0s 76us/step - loss: 145.5521 - regression_loss: 55.3490 - binary_classification_loss: 23.8978 - treatment_accuracy: 0.8529 - track_epsilon: 0.0014 - val_loss: 183.2418 - val_regression_loss: 59.2208 - val_binary_classification_loss: 35.7738 - val_treatment_accuracy: 0.7244 - val_track_epsilon: 0.0039\n",
      "\n",
      "Epoch 00017: ReduceLROnPlateau reducing learning rate to 4.999999873689376e-06.\n",
      "Epoch 18/300\n",
      "597/597 [==============================] - 0s 74us/step - loss: 144.7616 - regression_loss: 54.9449 - binary_classification_loss: 23.8797 - treatment_accuracy: 0.8561 - track_epsilon: 0.0050 - val_loss: 183.1350 - val_regression_loss: 59.2228 - val_binary_classification_loss: 35.7351 - val_treatment_accuracy: 0.7244 - val_track_epsilon: 0.0039\n",
      "Epoch 19/300\n",
      "597/597 [==============================] - 0s 67us/step - loss: 141.8471 - regression_loss: 54.6760 - binary_classification_loss: 23.8693 - treatment_accuracy: 0.8561 - track_epsilon: 0.0020 - val_loss: 182.4961 - val_regression_loss: 59.0138 - val_binary_classification_loss: 35.8385 - val_treatment_accuracy: 0.7244 - val_track_epsilon: 2.2382e-04\n",
      "Epoch 20/300\n",
      "597/597 [==============================] - 0s 75us/step - loss: 143.4988 - regression_loss: 54.6465 - binary_classification_loss: 23.8661 - treatment_accuracy: 0.8593 - track_epsilon: 9.6414e-04 - val_loss: 183.4780 - val_regression_loss: 59.2525 - val_binary_classification_loss: 35.8081 - val_treatment_accuracy: 0.7244 - val_track_epsilon: 6.3370e-04\n",
      "Epoch 21/300\n",
      "597/597 [==============================] - 0s 69us/step - loss: 143.2713 - regression_loss: 54.8240 - binary_classification_loss: 23.8655 - treatment_accuracy: 0.8529 - track_epsilon: 5.8381e-04 - val_loss: 182.7529 - val_regression_loss: 59.1405 - val_binary_classification_loss: 35.8905 - val_treatment_accuracy: 0.7244 - val_track_epsilon: 0.0014\n",
      "Epoch 22/300\n",
      "597/597 [==============================] - 0s 73us/step - loss: 144.5639 - regression_loss: 54.9520 - binary_classification_loss: 23.8562 - treatment_accuracy: 0.8497 - track_epsilon: 0.0011 - val_loss: 182.2272 - val_regression_loss: 58.9541 - val_binary_classification_loss: 35.8026 - val_treatment_accuracy: 0.7244 - val_track_epsilon: 0.0020\n",
      "Epoch 23/300\n",
      "597/597 [==============================] - 0s 88us/step - loss: 144.3322 - regression_loss: 54.4709 - binary_classification_loss: 23.8485 - treatment_accuracy: 0.8465 - track_epsilon: 0.0033 - val_loss: 183.0935 - val_regression_loss: 59.1250 - val_binary_classification_loss: 35.7517 - val_treatment_accuracy: 0.7244 - val_track_epsilon: 0.0026\n",
      "Epoch 24/300\n",
      "597/597 [==============================] - 0s 65us/step - loss: 143.6903 - regression_loss: 54.4800 - binary_classification_loss: 23.8423 - treatment_accuracy: 0.8561 - track_epsilon: 0.0013 - val_loss: 182.7994 - val_regression_loss: 59.0775 - val_binary_classification_loss: 35.7825 - val_treatment_accuracy: 0.7244 - val_track_epsilon: 0.0011\n",
      "\n",
      "Epoch 00024: ReduceLROnPlateau reducing learning rate to 2.499999936844688e-06.\n",
      "Epoch 25/300\n",
      "597/597 [==============================] - 0s 69us/step - loss: 142.5934 - regression_loss: 54.3459 - binary_classification_loss: 23.8378 - treatment_accuracy: 0.8529 - track_epsilon: 0.0012 - val_loss: 182.6808 - val_regression_loss: 59.0681 - val_binary_classification_loss: 35.7840 - val_treatment_accuracy: 0.7244 - val_track_epsilon: 0.0015\n",
      "Epoch 26/300\n",
      "597/597 [==============================] - 0s 69us/step - loss: 144.1265 - regression_loss: 54.4636 - binary_classification_loss: 23.8337 - treatment_accuracy: 0.8593 - track_epsilon: 0.0011 - val_loss: 183.0977 - val_regression_loss: 59.1001 - val_binary_classification_loss: 35.7414 - val_treatment_accuracy: 0.7244 - val_track_epsilon: 0.0011\n",
      "Epoch 27/300\n",
      "597/597 [==============================] - 0s 67us/step - loss: 143.5707 - regression_loss: 54.1999 - binary_classification_loss: 23.8293 - treatment_accuracy: 0.8497 - track_epsilon: 0.0016 - val_loss: 182.1685 - val_regression_loss: 58.8281 - val_binary_classification_loss: 35.7402 - val_treatment_accuracy: 0.7244 - val_track_epsilon: 0.0019\n",
      "Epoch 28/300\n",
      "597/597 [==============================] - 0s 70us/step - loss: 144.1436 - regression_loss: 54.1982 - binary_classification_loss: 23.8266 - treatment_accuracy: 0.8561 - track_epsilon: 0.0018 - val_loss: 182.2616 - val_regression_loss: 58.8418 - val_binary_classification_loss: 35.7468 - val_treatment_accuracy: 0.7244 - val_track_epsilon: 0.0017\n",
      "Epoch 29/300\n",
      "597/597 [==============================] - 0s 69us/step - loss: 143.1436 - regression_loss: 54.2246 - binary_classification_loss: 23.8253 - treatment_accuracy: 0.8497 - track_epsilon: 0.0017 - val_loss: 182.5233 - val_regression_loss: 58.9060 - val_binary_classification_loss: 35.7543 - val_treatment_accuracy: 0.7244 - val_track_epsilon: 0.0017\n",
      "\n",
      "Epoch 00029: ReduceLROnPlateau reducing learning rate to 1.249999968422344e-06.\n",
      "Epoch 30/300\n",
      "597/597 [==============================] - 0s 69us/step - loss: 142.9970 - regression_loss: 54.0639 - binary_classification_loss: 23.8208 - treatment_accuracy: 0.8625 - track_epsilon: 0.0016 - val_loss: 182.8976 - val_regression_loss: 59.0591 - val_binary_classification_loss: 35.7240 - val_treatment_accuracy: 0.7244 - val_track_epsilon: 0.0017\n",
      "Epoch 31/300\n",
      "597/597 [==============================] - 0s 67us/step - loss: 143.8003 - regression_loss: 54.1442 - binary_classification_loss: 23.8190 - treatment_accuracy: 0.8529 - track_epsilon: 0.0021 - val_loss: 182.6798 - val_regression_loss: 59.0072 - val_binary_classification_loss: 35.7270 - val_treatment_accuracy: 0.7244 - val_track_epsilon: 0.0022\n",
      "Epoch 32/300\n",
      "597/597 [==============================] - 0s 66us/step - loss: 143.4029 - regression_loss: 54.1355 - binary_classification_loss: 23.8157 - treatment_accuracy: 0.8561 - track_epsilon: 0.0023 - val_loss: 182.5541 - val_regression_loss: 58.9682 - val_binary_classification_loss: 35.7180 - val_treatment_accuracy: 0.7244 - val_track_epsilon: 0.0020\n",
      "Epoch 33/300\n",
      "597/597 [==============================] - 0s 73us/step - loss: 142.1901 - regression_loss: 54.0516 - binary_classification_loss: 23.8148 - treatment_accuracy: 0.8529 - track_epsilon: 0.0018 - val_loss: 183.0714 - val_regression_loss: 59.1151 - val_binary_classification_loss: 35.7216 - val_treatment_accuracy: 0.7244 - val_track_epsilon: 0.0015\n",
      "Epoch 34/300\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "597/597 [==============================] - 0s 65us/step - loss: 140.2360 - regression_loss: 54.0345 - binary_classification_loss: 23.8139 - treatment_accuracy: 0.8497 - track_epsilon: 0.0016 - val_loss: 182.7475 - val_regression_loss: 59.0084 - val_binary_classification_loss: 35.7426 - val_treatment_accuracy: 0.7244 - val_track_epsilon: 0.0015\n",
      "Epoch 35/300\n",
      "597/597 [==============================] - 0s 81us/step - loss: 142.8741 - regression_loss: 54.0038 - binary_classification_loss: 23.8122 - treatment_accuracy: 0.8433 - track_epsilon: 0.0013 - val_loss: 182.6587 - val_regression_loss: 58.9828 - val_binary_classification_loss: 35.7345 - val_treatment_accuracy: 0.7244 - val_track_epsilon: 0.0014\n",
      "Epoch 36/300\n",
      "597/597 [==============================] - 0s 73us/step - loss: 143.2542 - regression_loss: 54.0470 - binary_classification_loss: 23.8112 - treatment_accuracy: 0.8497 - track_epsilon: 0.0015 - val_loss: 182.7340 - val_regression_loss: 59.0171 - val_binary_classification_loss: 35.7291 - val_treatment_accuracy: 0.7244 - val_track_epsilon: 0.0016\n",
      "Epoch 37/300\n",
      "597/597 [==============================] - 0s 63us/step - loss: 143.1216 - regression_loss: 53.9242 - binary_classification_loss: 23.8101 - treatment_accuracy: 0.8497 - track_epsilon: 0.0018 - val_loss: 182.6380 - val_regression_loss: 58.9966 - val_binary_classification_loss: 35.7090 - val_treatment_accuracy: 0.7244 - val_track_epsilon: 0.0019\n",
      "Epoch 38/300\n",
      "597/597 [==============================] - 0s 74us/step - loss: 142.9598 - regression_loss: 53.9560 - binary_classification_loss: 23.8082 - treatment_accuracy: 0.8497 - track_epsilon: 0.0019 - val_loss: 182.5107 - val_regression_loss: 58.9566 - val_binary_classification_loss: 35.7025 - val_treatment_accuracy: 0.7244 - val_track_epsilon: 0.0018\n",
      "Epoch 39/300\n",
      "597/597 [==============================] - 0s 70us/step - loss: 142.1619 - regression_loss: 53.9813 - binary_classification_loss: 23.8070 - treatment_accuracy: 0.8497 - track_epsilon: 0.0015 - val_loss: 182.6606 - val_regression_loss: 58.9962 - val_binary_classification_loss: 35.7107 - val_treatment_accuracy: 0.7244 - val_track_epsilon: 0.0015\n",
      "\n",
      "Epoch 00039: ReduceLROnPlateau reducing learning rate to 6.24999984211172e-07.\n",
      "Epoch 40/300\n",
      "597/597 [==============================] - 0s 65us/step - loss: 143.1522 - regression_loss: 53.9099 - binary_classification_loss: 23.8051 - treatment_accuracy: 0.8561 - track_epsilon: 0.0017 - val_loss: 182.5675 - val_regression_loss: 58.9788 - val_binary_classification_loss: 35.6982 - val_treatment_accuracy: 0.7244 - val_track_epsilon: 0.0018\n",
      "Epoch 41/300\n",
      "597/597 [==============================] - 0s 70us/step - loss: 142.9669 - regression_loss: 54.0113 - binary_classification_loss: 23.8046 - treatment_accuracy: 0.8465 - track_epsilon: 0.0017 - val_loss: 182.7173 - val_regression_loss: 59.0139 - val_binary_classification_loss: 35.6968 - val_treatment_accuracy: 0.7244 - val_track_epsilon: 0.0018\n",
      "Epoch 42/300\n",
      "597/597 [==============================] - 0s 71us/step - loss: 142.9812 - regression_loss: 53.9480 - binary_classification_loss: 23.8039 - treatment_accuracy: 0.8625 - track_epsilon: 0.0019 - val_loss: 182.5140 - val_regression_loss: 58.9547 - val_binary_classification_loss: 35.7111 - val_treatment_accuracy: 0.7244 - val_track_epsilon: 0.0020\n",
      "Epoch 43/300\n",
      "597/597 [==============================] - 0s 73us/step - loss: 143.3660 - regression_loss: 53.9199 - binary_classification_loss: 23.8027 - treatment_accuracy: 0.8529 - track_epsilon: 0.0018 - val_loss: 182.6215 - val_regression_loss: 58.9790 - val_binary_classification_loss: 35.7084 - val_treatment_accuracy: 0.7244 - val_track_epsilon: 0.0018\n",
      "Epoch 44/300\n",
      "597/597 [==============================] - 0s 80us/step - loss: 142.2327 - regression_loss: 53.8626 - binary_classification_loss: 23.8023 - treatment_accuracy: 0.8561 - track_epsilon: 0.0019 - val_loss: 182.6031 - val_regression_loss: 58.9846 - val_binary_classification_loss: 35.7026 - val_treatment_accuracy: 0.7244 - val_track_epsilon: 0.0019\n",
      "\n",
      "Epoch 00044: ReduceLROnPlateau reducing learning rate to 3.12499992105586e-07.\n",
      "Epoch 45/300\n",
      "597/597 [==============================] - 0s 70us/step - loss: 141.4800 - regression_loss: 53.8688 - binary_classification_loss: 23.8012 - treatment_accuracy: 0.8497 - track_epsilon: 0.0018 - val_loss: 182.5443 - val_regression_loss: 58.9709 - val_binary_classification_loss: 35.7019 - val_treatment_accuracy: 0.7244 - val_track_epsilon: 0.0019\n",
      "Epoch 46/300\n",
      "597/597 [==============================] - 0s 75us/step - loss: 141.3155 - regression_loss: 53.8269 - binary_classification_loss: 23.8007 - treatment_accuracy: 0.8497 - track_epsilon: 0.0018 - val_loss: 182.6176 - val_regression_loss: 58.9883 - val_binary_classification_loss: 35.7016 - val_treatment_accuracy: 0.7244 - val_track_epsilon: 0.0018\n",
      "Epoch 47/300\n",
      "597/597 [==============================] - 0s 73us/step - loss: 143.1740 - regression_loss: 53.8460 - binary_classification_loss: 23.8005 - treatment_accuracy: 0.8497 - track_epsilon: 0.0018 - val_loss: 182.6459 - val_regression_loss: 59.0014 - val_binary_classification_loss: 35.6936 - val_treatment_accuracy: 0.7244 - val_track_epsilon: 0.0018\n",
      "Epoch 48/300\n",
      "597/597 [==============================] - 0s 85us/step - loss: 142.8012 - regression_loss: 53.8343 - binary_classification_loss: 23.7998 - treatment_accuracy: 0.8593 - track_epsilon: 0.0019 - val_loss: 182.6606 - val_regression_loss: 59.0031 - val_binary_classification_loss: 35.6939 - val_treatment_accuracy: 0.7244 - val_track_epsilon: 0.0019\n",
      "Epoch 49/300\n",
      "597/597 [==============================] - 0s 71us/step - loss: 142.5543 - regression_loss: 53.8559 - binary_classification_loss: 23.7995 - treatment_accuracy: 0.8497 - track_epsilon: 0.0019 - val_loss: 182.6408 - val_regression_loss: 58.9984 - val_binary_classification_loss: 35.6932 - val_treatment_accuracy: 0.7244 - val_track_epsilon: 0.0019\n",
      "\n",
      "Epoch 00049: ReduceLROnPlateau reducing learning rate to 1.56249996052793e-07.\n",
      "Epoch 50/300\n",
      "597/597 [==============================] - 0s 74us/step - loss: 142.2061 - regression_loss: 53.8305 - binary_classification_loss: 23.7990 - treatment_accuracy: 0.8465 - track_epsilon: 0.0019 - val_loss: 182.6622 - val_regression_loss: 59.0048 - val_binary_classification_loss: 35.6935 - val_treatment_accuracy: 0.7244 - val_track_epsilon: 0.0019\n",
      "Epoch 51/300\n",
      "597/597 [==============================] - 0s 71us/step - loss: 144.1153 - regression_loss: 53.8202 - binary_classification_loss: 23.7987 - treatment_accuracy: 0.8561 - track_epsilon: 0.0019 - val_loss: 182.6417 - val_regression_loss: 58.9983 - val_binary_classification_loss: 35.6931 - val_treatment_accuracy: 0.7244 - val_track_epsilon: 0.0019\n",
      "Epoch 52/300\n",
      "597/597 [==============================] - 0s 75us/step - loss: 142.2625 - regression_loss: 53.8170 - binary_classification_loss: 23.7987 - treatment_accuracy: 0.8497 - track_epsilon: 0.0018 - val_loss: 182.6301 - val_regression_loss: 58.9968 - val_binary_classification_loss: 35.6929 - val_treatment_accuracy: 0.7244 - val_track_epsilon: 0.0018\n",
      "Epoch 53/300\n",
      "597/597 [==============================] - 0s 77us/step - loss: 142.2695 - regression_loss: 53.8087 - binary_classification_loss: 23.7985 - treatment_accuracy: 0.8593 - track_epsilon: 0.0018 - val_loss: 182.6330 - val_regression_loss: 58.9989 - val_binary_classification_loss: 35.6917 - val_treatment_accuracy: 0.7244 - val_track_epsilon: 0.0018\n"
     ]
    }
   ],
   "source": [
    "dragon = DragonNet(neurons_per_layer=200, targeted_reg=True)\n",
    "dragon_ite = dragon.fit_predict(X, treatment, y, return_components=False)\n",
    "dragon_ate = dragon_ite.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-14T18:45:45.577739Z",
     "start_time": "2020-04-14T18:45:45.336472Z"
    }
   },
   "outputs": [],
   "source": [
    "df_preds = pd.DataFrame([s_ite.ravel(),\n",
    "                          t_ite.ravel(),\n",
    "                          x_ite.ravel(),\n",
    "                          r_ite.ravel(),\n",
    "                          dragon_ite.ravel(),\n",
    "                          tau.ravel(),\n",
    "                          treatment.ravel(),\n",
    "                          y.ravel()],\n",
    "                       index=['S','T','X','R','dragonnet','tau','w','y']).T\n",
    "\n",
    "df_cumgain = get_cumgain(df_preds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-14T18:45:45.751499Z",
     "start_time": "2020-04-14T18:45:45.583223Z"
    }
   },
   "outputs": [],
   "source": [
    "df_result = pd.DataFrame([s_ate, t_ate, x_ate, r_ate, dragon_ate, tau.mean()],\n",
    "                     index=['S','T','X','R','dragonnet','actual'], columns=['ATE'])\n",
    "df_result['MAE'] = [mean_absolute_error(t,p) for t,p in zip([s_ite, t_ite, x_ite, r_ite, dragon_ite],\n",
    "                                                            [tau.values.reshape(-1,1)]*5 )\n",
    "                ] + [None]\n",
    "df_result['AUUC'] = auuc_score(df_preds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-14T18:45:45.871610Z",
     "start_time": "2020-04-14T18:45:45.759641Z"
    }
   },
   "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>ATE</th>\n",
       "      <th>MAE</th>\n",
       "      <th>AUUC</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>S</th>\n",
       "      <td>4.054511</td>\n",
       "      <td>1.027666</td>\n",
       "      <td>0.575822</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>T</th>\n",
       "      <td>4.100199</td>\n",
       "      <td>0.980788</td>\n",
       "      <td>0.580929</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>X</th>\n",
       "      <td>4.020589</td>\n",
       "      <td>1.115693</td>\n",
       "      <td>0.564634</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>R</th>\n",
       "      <td>3.867016</td>\n",
       "      <td>2.033445</td>\n",
       "      <td>0.557536</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dragonnet</th>\n",
       "      <td>4.003578</td>\n",
       "      <td>1.182555</td>\n",
       "      <td>0.553948</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>actual</th>\n",
       "      <td>4.098887</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                ATE       MAE      AUUC\n",
       "S          4.054511  1.027666  0.575822\n",
       "T          4.100199  0.980788  0.580929\n",
       "X          4.020589  1.115693  0.564634\n",
       "R          3.867016  2.033445  0.557536\n",
       "dragonnet  4.003578  1.182555  0.553948\n",
       "actual     4.098887       NaN       NaN"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-14T18:45:46.547872Z",
     "start_time": "2020-04-14T18:45:45.874808Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_gain(df_preds)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# `causalml` Synthetic Data Generation Method"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-14T18:45:59.708335Z",
     "start_time": "2020-04-14T18:45:46.550221Z"
    }
   },
   "outputs": [],
   "source": [
    "y, X, w, tau, b, e = simulate_nuisance_and_easy_treatment(n=1000)\n",
    "\n",
    "X_train, X_val, y_train, y_val, w_train, w_val, tau_train, tau_val, b_train, b_val, e_train, e_val = \\\n",
    "    train_test_split(X, y, w, tau, b, e, test_size=0.2, random_state=123, shuffle=True)\n",
    "\n",
    "preds_dict_train = {}\n",
    "preds_dict_valid = {}\n",
    "\n",
    "preds_dict_train['Actuals'] = tau_train\n",
    "preds_dict_valid['Actuals'] = tau_val\n",
    "\n",
    "preds_dict_train['generated_data'] = {\n",
    "    'y': y_train,\n",
    "    'X': X_train,\n",
    "    'w': w_train,\n",
    "    'tau': tau_train,\n",
    "    'b': b_train,\n",
    "    'e': e_train}\n",
    "preds_dict_valid['generated_data'] = {\n",
    "    'y': y_val,\n",
    "    'X': X_val,\n",
    "    'w': w_val,\n",
    "    'tau': tau_val,\n",
    "    'b': b_val,\n",
    "    'e': e_val}\n",
    "\n",
    "# Predict p_hat because e would not be directly observed in real-life\n",
    "p_model = ElasticNetPropensityModel()\n",
    "p_hat_train = p_model.fit_predict(X_train, w_train)\n",
    "p_hat_val = p_model.fit_predict(X_val, w_val)\n",
    "\n",
    "for base_learner, label_l in zip([BaseSRegressor, BaseTRegressor, BaseXRegressor, BaseRRegressor],\n",
    "                                 ['S', 'T', 'X', 'R']):\n",
    "    for model, label_m in zip([LinearRegression, XGBRegressor], ['LR', 'XGB']):\n",
    "        # RLearner will need to fit on the p_hat\n",
    "        if label_l != 'R':\n",
    "            learner = base_learner(model())\n",
    "            # fit the model on training data only\n",
    "            learner.fit(X=X_train, treatment=w_train, y=y_train)\n",
    "            try:\n",
    "                preds_dict_train['{} Learner ({})'.format(\n",
    "                    label_l, label_m)] = learner.predict(X=X_train, p=p_hat_train).flatten()\n",
    "                preds_dict_valid['{} Learner ({})'.format(\n",
    "                    label_l, label_m)] = learner.predict(X=X_val, p=p_hat_val).flatten()\n",
    "            except TypeError:\n",
    "                preds_dict_train['{} Learner ({})'.format(\n",
    "                    label_l, label_m)] = learner.predict(X=X_train, treatment=w_train, y=y_train).flatten()\n",
    "                preds_dict_valid['{} Learner ({})'.format(\n",
    "                    label_l, label_m)] = learner.predict(X=X_val, treatment=w_val, y=y_val).flatten()\n",
    "        else:\n",
    "            learner = base_learner(model())\n",
    "            learner.fit(X=X_train, p=p_hat_train, treatment=w_train, y=y_train)\n",
    "            preds_dict_train['{} Learner ({})'.format(\n",
    "                label_l, label_m)] = learner.predict(X=X_train).flatten()\n",
    "            preds_dict_valid['{} Learner ({})'.format(\n",
    "                label_l, label_m)] = learner.predict(X=X_val).flatten()\n",
    "\n",
    "learner = DragonNet(verbose=False)\n",
    "learner.fit(X_train, treatment=w_train, y=y_train)\n",
    "preds_dict_train['DragonNet'] = learner.predict_tau(X=X_train).flatten()\n",
    "preds_dict_valid['DragonNet'] = learner.predict_tau(X=X_val).flatten()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-14T18:45:59.843498Z",
     "start_time": "2020-04-14T18:45:59.711221Z"
    }
   },
   "outputs": [],
   "source": [
    "actuals_train = preds_dict_train['Actuals']\n",
    "actuals_validation = preds_dict_valid['Actuals']\n",
    "\n",
    "synthetic_summary_train = pd.DataFrame({label: [preds.mean(), mse(preds, actuals_train)] for label, preds\n",
    "                                        in preds_dict_train.items() if 'generated' not in label.lower()},\n",
    "                                       index=['ATE', 'MSE']).T\n",
    "synthetic_summary_train['Abs % Error of ATE'] = np.abs(\n",
    "    (synthetic_summary_train['ATE']/synthetic_summary_train.loc['Actuals', 'ATE']) - 1)\n",
    "\n",
    "synthetic_summary_validation = pd.DataFrame({label: [preds.mean(), mse(preds, actuals_validation)]\n",
    "                                             for label, preds in preds_dict_valid.items()\n",
    "                                             if 'generated' not in label.lower()},\n",
    "                                            index=['ATE', 'MSE']).T\n",
    "synthetic_summary_validation['Abs % Error of ATE'] = np.abs(\n",
    "    (synthetic_summary_validation['ATE']/synthetic_summary_validation.loc['Actuals', 'ATE']) - 1)\n",
    "\n",
    "# calculate kl divergence for training\n",
    "for label in synthetic_summary_train.index:\n",
    "    stacked_values = np.hstack((preds_dict_train[label], actuals_train))\n",
    "    stacked_low = np.percentile(stacked_values, 0.1)\n",
    "    stacked_high = np.percentile(stacked_values, 99.9)\n",
    "    bins = np.linspace(stacked_low, stacked_high, 100)\n",
    "\n",
    "    distr = np.histogram(preds_dict_train[label], bins=bins)[0]\n",
    "    distr = np.clip(distr/distr.sum(), 0.001, 0.999)\n",
    "    true_distr = np.histogram(actuals_train, bins=bins)[0]\n",
    "    true_distr = np.clip(true_distr/true_distr.sum(), 0.001, 0.999)\n",
    "\n",
    "    kl = entropy(distr, true_distr)\n",
    "    synthetic_summary_train.loc[label, 'KL Divergence'] = kl\n",
    "\n",
    "# calculate kl divergence for validation\n",
    "for label in synthetic_summary_validation.index:\n",
    "    stacked_values = np.hstack((preds_dict_valid[label], actuals_validation))\n",
    "    stacked_low = np.percentile(stacked_values, 0.1)\n",
    "    stacked_high = np.percentile(stacked_values, 99.9)\n",
    "    bins = np.linspace(stacked_low, stacked_high, 100)\n",
    "\n",
    "    distr = np.histogram(preds_dict_valid[label], bins=bins)[0]\n",
    "    distr = np.clip(distr/distr.sum(), 0.001, 0.999)\n",
    "    true_distr = np.histogram(actuals_validation, bins=bins)[0]\n",
    "    true_distr = np.clip(true_distr/true_distr.sum(), 0.001, 0.999)\n",
    "\n",
    "    kl = entropy(distr, true_distr)\n",
    "    synthetic_summary_validation.loc[label, 'KL Divergence'] = kl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-14T18:46:00.042356Z",
     "start_time": "2020-04-14T18:45:59.846526Z"
    }
   },
   "outputs": [],
   "source": [
    "df_preds_train = pd.DataFrame([preds_dict_train['S Learner (LR)'].ravel(),\n",
    "                               preds_dict_train['S Learner (XGB)'].ravel(),\n",
    "                               preds_dict_train['T Learner (LR)'].ravel(),\n",
    "                               preds_dict_train['T Learner (XGB)'].ravel(),\n",
    "                               preds_dict_train['X Learner (LR)'].ravel(),\n",
    "                               preds_dict_train['X Learner (XGB)'].ravel(),\n",
    "                               preds_dict_train['R Learner (LR)'].ravel(),\n",
    "                               preds_dict_train['R Learner (XGB)'].ravel(),                               \n",
    "                               preds_dict_train['DragonNet'].ravel(),\n",
    "                               preds_dict_train['generated_data']['tau'].ravel(),\n",
    "                               preds_dict_train['generated_data']['w'].ravel(),\n",
    "                               preds_dict_train['generated_data']['y'].ravel()],\n",
    "                              index=['S Learner (LR)','S Learner (XGB)',\n",
    "                                     'T Learner (LR)','T Learner (XGB)',\n",
    "                                     'X Learner (LR)','X Learner (XGB)',\n",
    "                                     'R Learner (LR)','R Learner (XGB)',\n",
    "                                     'DragonNet','tau','w','y']).T\n",
    "\n",
    "synthetic_summary_train['AUUC'] = auuc_score(df_preds_train).iloc[:-1]\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-14T18:46:00.186559Z",
     "start_time": "2020-04-14T18:46:00.045033Z"
    }
   },
   "outputs": [],
   "source": [
    "df_preds_validation = pd.DataFrame([preds_dict_valid['S Learner (LR)'].ravel(),\n",
    "                               preds_dict_valid['S Learner (XGB)'].ravel(),\n",
    "                               preds_dict_valid['T Learner (LR)'].ravel(),\n",
    "                               preds_dict_valid['T Learner (XGB)'].ravel(),\n",
    "                               preds_dict_valid['X Learner (LR)'].ravel(),\n",
    "                               preds_dict_valid['X Learner (XGB)'].ravel(),\n",
    "                               preds_dict_valid['R Learner (LR)'].ravel(),\n",
    "                               preds_dict_valid['R Learner (XGB)'].ravel(),                               \n",
    "                               preds_dict_valid['DragonNet'].ravel(),\n",
    "                               preds_dict_valid['generated_data']['tau'].ravel(),\n",
    "                               preds_dict_valid['generated_data']['w'].ravel(),\n",
    "                               preds_dict_valid['generated_data']['y'].ravel()],\n",
    "                              index=['S Learner (LR)','S Learner (XGB)',\n",
    "                                     'T Learner (LR)','T Learner (XGB)',\n",
    "                                     'X Learner (LR)','X Learner (XGB)',\n",
    "                                     'R Learner (LR)','R Learner (XGB)',\n",
    "                                     'DragonNet','tau','w','y']).T\n",
    "\n",
    "synthetic_summary_validation['AUUC'] = auuc_score(df_preds_validation).iloc[:-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-14T18:46:00.277936Z",
     "start_time": "2020-04-14T18:46:00.189200Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\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>ATE</th>\n",
       "      <th>MSE</th>\n",
       "      <th>Abs % Error of ATE</th>\n",
       "      <th>KL Divergence</th>\n",
       "      <th>AUUC</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Actuals</th>\n",
       "      <td>0.484486</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S Learner (LR)</th>\n",
       "      <td>0.528743</td>\n",
       "      <td>0.044194</td>\n",
       "      <td>0.091349</td>\n",
       "      <td>3.473087</td>\n",
       "      <td>0.492660</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S Learner (XGB)</th>\n",
       "      <td>0.358208</td>\n",
       "      <td>0.310652</td>\n",
       "      <td>0.260643</td>\n",
       "      <td>0.817620</td>\n",
       "      <td>0.544115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>T Learner (LR)</th>\n",
       "      <td>0.493815</td>\n",
       "      <td>0.022688</td>\n",
       "      <td>0.019255</td>\n",
       "      <td>0.289978</td>\n",
       "      <td>0.610855</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>T Learner (XGB)</th>\n",
       "      <td>0.397053</td>\n",
       "      <td>1.350928</td>\n",
       "      <td>0.180465</td>\n",
       "      <td>1.452143</td>\n",
       "      <td>0.521719</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>X Learner (LR)</th>\n",
       "      <td>0.493815</td>\n",
       "      <td>0.022688</td>\n",
       "      <td>0.019255</td>\n",
       "      <td>0.289978</td>\n",
       "      <td>0.610855</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>X Learner (XGB)</th>\n",
       "      <td>0.341013</td>\n",
       "      <td>0.620823</td>\n",
       "      <td>0.296134</td>\n",
       "      <td>1.098308</td>\n",
       "      <td>0.534908</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>R Learner (LR)</th>\n",
       "      <td>0.471610</td>\n",
       "      <td>0.030968</td>\n",
       "      <td>0.026577</td>\n",
       "      <td>0.378494</td>\n",
       "      <td>0.614607</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>R Learner (XGB)</th>\n",
       "      <td>0.413902</td>\n",
       "      <td>4.850255</td>\n",
       "      <td>0.145688</td>\n",
       "      <td>1.950556</td>\n",
       "      <td>0.510872</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DragonNet</th>\n",
       "      <td>0.415214</td>\n",
       "      <td>0.038613</td>\n",
       "      <td>0.142980</td>\n",
       "      <td>0.405291</td>\n",
       "      <td>0.612157</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      ATE       MSE  Abs % Error of ATE  KL Divergence  \\\n",
       "Actuals          0.484486  0.000000            0.000000       0.000000   \n",
       "S Learner (LR)   0.528743  0.044194            0.091349       3.473087   \n",
       "S Learner (XGB)  0.358208  0.310652            0.260643       0.817620   \n",
       "T Learner (LR)   0.493815  0.022688            0.019255       0.289978   \n",
       "T Learner (XGB)  0.397053  1.350928            0.180465       1.452143   \n",
       "X Learner (LR)   0.493815  0.022688            0.019255       0.289978   \n",
       "X Learner (XGB)  0.341013  0.620823            0.296134       1.098308   \n",
       "R Learner (LR)   0.471610  0.030968            0.026577       0.378494   \n",
       "R Learner (XGB)  0.413902  4.850255            0.145688       1.950556   \n",
       "DragonNet        0.415214  0.038613            0.142980       0.405291   \n",
       "\n",
       "                     AUUC  \n",
       "Actuals               NaN  \n",
       "S Learner (LR)   0.492660  \n",
       "S Learner (XGB)  0.544115  \n",
       "T Learner (LR)   0.610855  \n",
       "T Learner (XGB)  0.521719  \n",
       "X Learner (LR)   0.610855  \n",
       "X Learner (XGB)  0.534908  \n",
       "R Learner (LR)   0.614607  \n",
       "R Learner (XGB)  0.510872  \n",
       "DragonNet        0.612157  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "synthetic_summary_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-14T18:46:00.367971Z",
     "start_time": "2020-04-14T18:46:00.280588Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\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>ATE</th>\n",
       "      <th>MSE</th>\n",
       "      <th>Abs % Error of ATE</th>\n",
       "      <th>KL Divergence</th>\n",
       "      <th>AUUC</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Actuals</th>\n",
       "      <td>0.511242</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S Learner (LR)</th>\n",
       "      <td>0.528743</td>\n",
       "      <td>0.042236</td>\n",
       "      <td>0.034233</td>\n",
       "      <td>4.574498</td>\n",
       "      <td>0.494022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S Learner (XGB)</th>\n",
       "      <td>0.434208</td>\n",
       "      <td>0.260496</td>\n",
       "      <td>0.150680</td>\n",
       "      <td>0.854890</td>\n",
       "      <td>0.544212</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>T Learner (LR)</th>\n",
       "      <td>0.541503</td>\n",
       "      <td>0.025840</td>\n",
       "      <td>0.059191</td>\n",
       "      <td>0.686602</td>\n",
       "      <td>0.604712</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>T Learner (XGB)</th>\n",
       "      <td>0.483404</td>\n",
       "      <td>0.679398</td>\n",
       "      <td>0.054451</td>\n",
       "      <td>1.215394</td>\n",
       "      <td>0.526918</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>X Learner (LR)</th>\n",
       "      <td>0.541503</td>\n",
       "      <td>0.025840</td>\n",
       "      <td>0.059191</td>\n",
       "      <td>0.686602</td>\n",
       "      <td>0.604712</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>X Learner (XGB)</th>\n",
       "      <td>0.328046</td>\n",
       "      <td>0.352812</td>\n",
       "      <td>0.358335</td>\n",
       "      <td>1.310631</td>\n",
       "      <td>0.535895</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>R Learner (LR)</th>\n",
       "      <td>0.526797</td>\n",
       "      <td>0.034872</td>\n",
       "      <td>0.030426</td>\n",
       "      <td>0.732823</td>\n",
       "      <td>0.608290</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>R Learner (XGB)</th>\n",
       "      <td>0.377533</td>\n",
       "      <td>2.174835</td>\n",
       "      <td>0.261537</td>\n",
       "      <td>1.734253</td>\n",
       "      <td>0.512412</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DragonNet</th>\n",
       "      <td>0.464221</td>\n",
       "      <td>0.037349</td>\n",
       "      <td>0.091973</td>\n",
       "      <td>0.695660</td>\n",
       "      <td>0.606139</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      ATE       MSE  Abs % Error of ATE  KL Divergence  \\\n",
       "Actuals          0.511242  0.000000            0.000000       0.000000   \n",
       "S Learner (LR)   0.528743  0.042236            0.034233       4.574498   \n",
       "S Learner (XGB)  0.434208  0.260496            0.150680       0.854890   \n",
       "T Learner (LR)   0.541503  0.025840            0.059191       0.686602   \n",
       "T Learner (XGB)  0.483404  0.679398            0.054451       1.215394   \n",
       "X Learner (LR)   0.541503  0.025840            0.059191       0.686602   \n",
       "X Learner (XGB)  0.328046  0.352812            0.358335       1.310631   \n",
       "R Learner (LR)   0.526797  0.034872            0.030426       0.732823   \n",
       "R Learner (XGB)  0.377533  2.174835            0.261537       1.734253   \n",
       "DragonNet        0.464221  0.037349            0.091973       0.695660   \n",
       "\n",
       "                     AUUC  \n",
       "Actuals               NaN  \n",
       "S Learner (LR)   0.494022  \n",
       "S Learner (XGB)  0.544212  \n",
       "T Learner (LR)   0.604712  \n",
       "T Learner (XGB)  0.526918  \n",
       "X Learner (LR)   0.604712  \n",
       "X Learner (XGB)  0.535895  \n",
       "R Learner (LR)   0.608290  \n",
       "R Learner (XGB)  0.512412  \n",
       "DragonNet        0.606139  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "synthetic_summary_validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-14T18:46:00.919031Z",
     "start_time": "2020-04-14T18:46:00.374917Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_gain(df_preds_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-14T18:46:01.520118Z",
     "start_time": "2020-04-14T18:46:00.921831Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_gain(df_preds_validation)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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