gformula.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import dowhy.datasets\n",
    "\n",
    "\n",
    "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",
    "df = data[\"df\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from dowhy.causal_estimators.gformula_estimator import GFormulaEstimator\n",
    "from dowhy.do_why import CausalModel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "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:{'X2', 'X0', 'U', 'X3', 'X1', 'X4'}\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['X2', 'X0', 'X3', 'X1', 'X4']\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",
      "{'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] yes\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:dowhy.causal_identifier:Instrumental variables for treatment and outcome:[]\n"
     ]
    }
   ],
   "source": [
    "model= CausalModel(\n",
    "        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": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:dowhy.causal_estimator:INFO: Using Linear Robins G-Formula Estimator\n",
      "INFO:dowhy.causal_estimator:b: y~v+X2+X0+X3+X1+X4\n"
     ]
    }
   ],
   "source": [
    "test_significance=False\n",
    "estimator = GFormulaEstimator(df, identified_estimand, 'v', 'y',\n",
    "                              test_significance, params=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/latex": [
       "$$10.00000000000001$$"
      ],
      "text/plain": [
       "10.00000000000001"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "estimator.estimate_outcome(1.) - estimator.estimate_outcome(0.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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