linear_do.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "from dowhy.do_why import CausalModel\n",
    "import dowhy.datasets\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = dowhy.datasets.linear_dataset(beta=10,\n",
    "        num_common_causes=5,\n",
    "        num_instruments = 2,\n",
    "        num_samples=1000,\n",
    "        treatment_is_binary=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:dowhy.do_why:Causal Graph not provided. DoWhy will construct a graph based on data inputs.\n",
      "INFO:dowhy.causal_identifier:Common causes of treatment and outcome:{'X3', 'X0', 'X1', 'X4', 'U', 'X2'}\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['X3', 'X1', 'X0', 'X4', 'X2']\n",
      "yes\n",
      "{'observed': 'yes'}\n",
      "yes\n",
      "{'observed': 'yes'}\n",
      "yes\n",
      "{'observed': 'yes'}\n",
      "yes\n",
      "{'observed': 'yes'}\n",
      "yes\n",
      "{'observed': 'yes'}\n",
      "Model to find the causal effect of treatment v on outcome y\n",
      "{'observed': 'yes'}\n",
      "{'observed': 'yes'}\n",
      "{'observed': 'yes'}\n",
      "{'observed': 'yes'}\n",
      "{'label': 'Unobserved Confounders', 'observed': 'no'}\n",
      "There are unobserved common causes. Causal effect cannot be identified.\n",
      "WARN: Do you want to continue by ignoring these unobserved confounders? [y/n] \n",
      "Please respond with 'y' or 'n'\n",
      "WARN: Do you want to continue by ignoring these unobserved confounders? [y/n] \n",
      "Please respond with 'y' or 'n'\n",
      "WARN: Do you want to continue by ignoring these unobserved confounders? [y/n] yes\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:dowhy.causal_identifier:Instrumental variables for treatment and outcome:[]\n"
     ]
    }
   ],
   "source": [
    "model= CausalModel(\n",
    "        data=data[\"df\"],\n",
    "        treatment=data[\"treatment_name\"],\n",
    "        outcome=data[\"outcome_name\"],\n",
    "        common_causes=data[\"common_causes_names\"])\n",
    "\n",
    "identified_estimand = model.identify_effect()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:dowhy.causal_estimator:INFO: Using Linear Regression Estimator\n",
      "INFO:dowhy.causal_estimator:b: y~v+X3+X0+X1+X4+X2\n",
      "INFO:dowhy.causal_estimator:INFO: Using Linear Regression Estimator\n",
      "INFO:dowhy.causal_estimator:b: y~v+X3+X0+X1+X4+X2\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LinearRegressionEstimator\n",
      "LinearRegressionEstimator\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/latex": [
       "$$10.000000000000002$$"
      ],
      "text/plain": [
       "10.000000000000002"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.do(1., \n",
    "         identified_estimand=identified_estimand,\n",
    "         method_name=\"backdoor.linear_regression\",\n",
    "         test_significance=False) - \\\n",
    "model.do(0., \n",
    "         identified_estimand=identified_estimand,\n",
    "         method_name=\"backdoor.linear_regression\",\n",
    "         test_significance=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}