test_approx_bound.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "from estimator_model.approximation_bound import CausalEffectBound\n",
    "from exp_data import coupon_dataset, meaningless_discrete_dataset_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "num = 1000\n",
    "np.random.seed(2022)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>treatment</th>\n",
       "      <th>w_0</th>\n",
       "      <th>w_1</th>\n",
       "      <th>w_2</th>\n",
       "      <th>outcome</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2.034978</td>\n",
       "      <td>-0.308326</td>\n",
       "      <td>-1.752033</td>\n",
       "      <td>2.934636</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>4.261300</td>\n",
       "      <td>-0.096214</td>\n",
       "      <td>-2.716450</td>\n",
       "      <td>1.434828</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>-6.224446</td>\n",
       "      <td>0.149369</td>\n",
       "      <td>-1.432466</td>\n",
       "      <td>0.582026</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>3.659339</td>\n",
       "      <td>0.239779</td>\n",
       "      <td>-0.321641</td>\n",
       "      <td>4.163927</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>-2.312492</td>\n",
       "      <td>-0.113462</td>\n",
       "      <td>-1.492318</td>\n",
       "      <td>-9.578131</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "   treatment       w_0       w_1       w_2   outcome\n",
       "0          1  2.034978 -0.308326 -1.752033  2.934636\n",
       "1          1  4.261300 -0.096214 -2.716450  1.434828\n",
       "2          0 -6.224446  0.149369 -1.432466  0.582026\n",
       "3          1  3.659339  0.239779 -0.321641  4.163927\n",
       "4          2 -2.312492 -0.113462 -1.492318 -9.578131"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = meaningless_discrete_dataset_(num=num,\n",
    "                                    confounder_n=3,\n",
    "                                    treatment_effct=[2, 5, -8],\n",
    "                                    random_seed=0)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    464\n",
       "0    401\n",
       "2    135\n",
       "Name: treatment, dtype: int64"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "treatment = 'treatment'\n",
    "w = ['w_0', 'w_1', 'w_2']\n",
    "outcome = 'outcome'\n",
    "data[treatment].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "y_model = LinearRegression()\n",
    "bound = CausalEffectBound(y_model=y_model, \n",
    "                          data=data, \n",
    "                          outcome=outcome,\n",
    "                          treatment=treatment)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "bound.fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True     985\n",
       "False     15\n",
       "Name: outcome, dtype: int64"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(data[outcome].sort_values() < 8).value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True     972\n",
       "False     28\n",
       "Name: outcome, dtype: int64"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(data[outcome].sort_values() > -10).value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0, array([[9.03178673]]))"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bound.effect_bound(treatment_value=1,\n",
    "                   y_upper=8,\n",
    "                   y_lower=-10,\n",
    "                   assump='non-negative')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([[-20.15550165]]), 0)"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bound.effect_bound(treatment_value=2,\n",
    "                   assump='non-positive')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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