1_13_2018_Foong_Debugging Causal Analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "from causalinference import CausalModel\n",
    "from causalinference.utils import random_data\n",
    "Y, D, X = random_data()\n",
    "causal = CausalModel(Y, D, X)\n",
    "\n",
    "log = open('trial.txt', 'a')\n",
    "log.write('Hello world')\n",
    "\n",
    "log.write(str(causal.summary_stats))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Summary Statistics\n",
      "\n",
      "                      Controls (N_c=2474)        Treated (N_t=2526)             \n",
      "       Variable         Mean         S.d.         Mean         S.d.     Raw-diff\n",
      "--------------------------------------------------------------------------------\n",
      "              Y       -0.979        1.731        4.999        3.038        5.979\n",
      "\n",
      "                      Controls (N_c=2474)        Treated (N_t=2526)             \n",
      "       Variable         Mean         S.d.         Mean         S.d.     Nor-diff\n",
      "--------------------------------------------------------------------------------\n",
      "             X0       -0.298        0.942        0.309        0.946        0.643\n",
      "             X1       -0.331        0.952        0.365        0.945        0.734\n",
      "             X2       -0.325        0.953        0.321        0.940        0.683\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(causal.summary_stats)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 1.20050092 -1.6234815  -4.1039495  ...,  3.69938636  6.97564572\n",
      "  5.53833445]\n"
     ]
    }
   ],
   "source": [
    "print Y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5000\n"
     ]
    }
   ],
   "source": [
    "print len(Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-1.62348150294\n"
     ]
    }
   ],
   "source": [
    "print Y[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5000\n"
     ]
    }
   ],
   "source": [
    "print len(D)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 0 0 ..., 1 1 1]\n"
     ]
    }
   ],
   "source": [
    "print D"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<type 'numpy.ndarray'>\n"
     ]
    }
   ],
   "source": [
    "print type(D)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 0.11397722 -1.50632986  1.82324589]\n",
      " [ 0.33427711 -1.37102103 -0.54178945]\n",
      " [-1.6984341  -1.45582941 -0.16334798]\n",
      " ..., \n",
      " [ 0.18455614  0.49899026 -0.61167924]\n",
      " [ 0.44975097 -0.74715083  2.16987318]\n",
      " [-1.05599124  1.79133336  0.42363047]]\n"
     ]
    }
   ],
   "source": [
    "print X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<type 'numpy.ndarray'>\n"
     ]
    }
   ],
   "source": [
    "print type(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Treatment Effect Estimates: Matching\n",
      "\n",
      "                     Est.       S.e.          z      P>|z|      [95% Conf. int.]\n",
      "--------------------------------------------------------------------------------\n",
      "           ATE      3.272      0.089     36.843      0.000      3.098      3.446\n",
      "           ATC      2.328      0.119     19.489      0.000      2.094      2.562\n",
      "           ATT      4.196      0.103     40.797      0.000      3.994      4.398\n",
      "\n"
     ]
    }
   ],
   "source": [
    "causal.est_via_matching()\n",
    "print(causal.estimates)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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