tests/experimental.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys\n",
    "sys.path.insert(0,'..')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
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     "text": [
      "[0. 1. 1. 1. 0. 0. 0. 0. 1. 1.]\n",
      "[0. 1. 0. 0. 1. 0. 1. 0. 0. 0. 1. 1. 0. 0. 0. 1. 0. 1. 0. 1. 1. 0. 1. 1.\n",
      " 0. 1. 1. 0. 1. 1. 0. 0. 0. 0. 0. 0. 1. 1. 0. 1. 0. 1. 1. 0. 0. 0. 1. 1.\n",
      " 0. 1. 1. 1. 1. 0. 1. 1. 1. 0. 0. 1. 1. 1. 0. 1. 0. 0. 1. 1. 1. 0. 1. 1.\n",
      " 0. 0. 0. 1. 1. 0. 1. 1. 1. 1. 0. 0. 0. 1. 0. 1. 0. 0. 0. 1. 0. 0. 1. 0.\n",
      " 0. 0. 1. 1.]\n",
      "[0 1 1 1 1 1 0 1 1 1]\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "from causalmodel.designs import DesignBase, CRD, Bernoulli\n",
    "\n",
    "crd = CRD()\n",
    "print(crd.draw(10))\n",
    "\n",
    "X = np.random.normal(0,1,size=(100,2))\n",
    "crd = CRD(covariate=X, balance=True)\n",
    "print(crd.draw(100))\n",
    "\n",
    "bnli = Bernoulli()\n",
    "print(bnli.draw(10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "********************\n",
      "average treatment effect: 11.448833445647926\n",
      "\n",
      "standard error:           0.03148482173522997\n",
      "\n",
      "p value:                  0.0\n",
      "\n",
      "confidence interval:      (11.387123195046875, 11.510543696248977)\n"
     ]
    }
   ],
   "source": [
    "from causalmodel.experimental import Experimental\n",
    "from causalmodel.utils.random_data import get_data\n",
    "\n",
    "Y, Z, X = get_data()\n",
    "exp = Experimental(Y, Z, X)\n",
    "exp.estimate().show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "********************\n",
      "average treatment effect: 11.448587167513448\n",
      "\n",
      "standard error:           0.03147594488830996\n",
      "\n",
      "p value:                  0.0\n",
      "\n",
      "confidence interval:      (11.386894315532361, 11.510280019494536)\n"
     ]
    }
   ],
   "source": [
    "strata = np.random.choice([0,1,2],len(Y))\n",
    "exp.est_via_strata(strata).show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "********************\n",
      "average treatment effect: 9.999044281740225\n",
      "\n",
      "standard error:           0.023604890995131526\n",
      "\n",
      "p value:                  0.0\n",
      "\n",
      "confidence interval:      (9.952778695389767, 10.045309868090683)\n"
     ]
    }
   ],
   "source": [
    "exp.est_via_ancova().show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "exp.test_via_fisher()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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