{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Difference in Differences with sci-kit learn models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "import causalpy as cp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = cp.load_data(\"did\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "result = cp.DifferenceInDifferences(\n",
    "    data,\n",
    "    formula=\"y ~ 1 + group*post_treatment\",\n",
    "    time_variable_name=\"t\",\n",
    "    group_variable_name=\"group\",\n",
    "    model=LinearRegression(),\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/benjamv/opt/mambaforge/envs/CausalPy/lib/python3.11/site-packages/IPython/core/pylabtools.py:77: DeprecationWarning: backend2gui is deprecated since IPython 8.24, backends are managed in matplotlib and can be externally registered.\n",
      "  warnings.warn(\n",
      "/Users/benjamv/opt/mambaforge/envs/CausalPy/lib/python3.11/site-packages/IPython/core/pylabtools.py:77: DeprecationWarning: backend2gui is deprecated since IPython 8.24, backends are managed in matplotlib and can be externally registered.\n",
      "  warnings.warn(\n",
      "/Users/benjamv/opt/mambaforge/envs/CausalPy/lib/python3.11/site-packages/IPython/core/pylabtools.py:77: DeprecationWarning: backend2gui is deprecated since IPython 8.24, backends are managed in matplotlib and can be externally registered.\n",
      "  warnings.warn(\n",
      "/Users/benjamv/opt/mambaforge/envs/CausalPy/lib/python3.11/site-packages/matplotlib/text.py:1463: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
      "  y = float(self.convert_yunits(y))\n",
      "/Users/benjamv/opt/mambaforge/envs/CausalPy/lib/python3.11/site-packages/matplotlib/text.py:1463: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
      "  y = float(self.convert_yunits(y))\n",
      "/Users/benjamv/opt/mambaforge/envs/CausalPy/lib/python3.11/site-packages/matplotlib/text.py:895: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
      "  y = float(self.convert_yunits(self._y))\n",
      "/Users/benjamv/opt/mambaforge/envs/CausalPy/lib/python3.11/site-packages/matplotlib/text.py:1463: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
      "  y = float(self.convert_yunits(y))\n",
      "/Users/benjamv/opt/mambaforge/envs/CausalPy/lib/python3.11/site-packages/matplotlib/text.py:895: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
      "  y = float(self.convert_yunits(self._y))\n",
      "/Users/benjamv/opt/mambaforge/envs/CausalPy/lib/python3.11/site-packages/matplotlib/text.py:1463: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
      "  y = float(self.convert_yunits(y))\n",
      "/Users/benjamv/opt/mambaforge/envs/CausalPy/lib/python3.11/site-packages/matplotlib/text.py:1463: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
      "  y = float(self.convert_yunits(y))\n",
      "/Users/benjamv/opt/mambaforge/envs/CausalPy/lib/python3.11/site-packages/matplotlib/text.py:895: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
      "  y = float(self.convert_yunits(self._y))\n",
      "/Users/benjamv/opt/mambaforge/envs/CausalPy/lib/python3.11/site-packages/matplotlib/text.py:1463: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
      "  y = float(self.convert_yunits(y))\n",
      "/Users/benjamv/opt/mambaforge/envs/CausalPy/lib/python3.11/site-packages/matplotlib/text.py:895: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
      "  y = float(self.convert_yunits(self._y))\n",
      "/Users/benjamv/opt/mambaforge/envs/CausalPy/lib/python3.11/site-packages/matplotlib/text.py:1463: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
      "  y = float(self.convert_yunits(y))\n",
      "/Users/benjamv/opt/mambaforge/envs/CausalPy/lib/python3.11/site-packages/matplotlib/text.py:895: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
      "  y = float(self.convert_yunits(self._y))\n",
      "/Users/benjamv/opt/mambaforge/envs/CausalPy/lib/python3.11/site-packages/matplotlib/text.py:1463: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
      "  y = float(self.convert_yunits(y))\n",
      "/Users/benjamv/opt/mambaforge/envs/CausalPy/lib/python3.11/site-packages/matplotlib/text.py:1463: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
      "  y = float(self.convert_yunits(y))\n",
      "/Users/benjamv/opt/mambaforge/envs/CausalPy/lib/python3.11/site-packages/matplotlib/text.py:1463: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
      "  y = float(self.convert_yunits(y))\n",
      "/Users/benjamv/opt/mambaforge/envs/CausalPy/lib/python3.11/site-packages/matplotlib/text.py:895: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
      "  y = float(self.convert_yunits(self._y))\n",
      "/Users/benjamv/opt/mambaforge/envs/CausalPy/lib/python3.11/site-packages/matplotlib/text.py:1463: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
      "  y = float(self.convert_yunits(y))\n",
      "/Users/benjamv/opt/mambaforge/envs/CausalPy/lib/python3.11/site-packages/matplotlib/text.py:895: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
      "  y = float(self.convert_yunits(self._y))\n",
      "/Users/benjamv/opt/mambaforge/envs/CausalPy/lib/python3.11/site-packages/matplotlib/text.py:1463: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
      "  y = float(self.convert_yunits(y))\n",
      "/Users/benjamv/opt/mambaforge/envs/CausalPy/lib/python3.11/site-packages/matplotlib/text.py:1463: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
      "  y = float(self.convert_yunits(y))\n",
      "/Users/benjamv/opt/mambaforge/envs/CausalPy/lib/python3.11/site-packages/matplotlib/text.py:895: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
      "  y = float(self.convert_yunits(self._y))\n",
      "/Users/benjamv/opt/mambaforge/envs/CausalPy/lib/python3.11/site-packages/matplotlib/text.py:1463: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
      "  y = float(self.convert_yunits(y))\n",
      "/Users/benjamv/opt/mambaforge/envs/CausalPy/lib/python3.11/site-packages/matplotlib/text.py:895: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
      "  y = float(self.convert_yunits(self._y))\n",
      "/Users/benjamv/opt/mambaforge/envs/CausalPy/lib/python3.11/site-packages/matplotlib/text.py:1463: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
      "  y = float(self.convert_yunits(y))\n",
      "/Users/benjamv/opt/mambaforge/envs/CausalPy/lib/python3.11/site-packages/matplotlib/text.py:895: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
      "  y = float(self.convert_yunits(self._y))\n",
      "/Users/benjamv/opt/mambaforge/envs/CausalPy/lib/python3.11/site-packages/matplotlib/text.py:1463: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
      "  y = float(self.convert_yunits(y))\n",
      "/Users/benjamv/opt/mambaforge/envs/CausalPy/lib/python3.11/site-packages/matplotlib/text.py:1463: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
      "  y = float(self.convert_yunits(y))\n",
      "/Users/benjamv/opt/mambaforge/envs/CausalPy/lib/python3.11/site-packages/matplotlib/text.py:1463: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
      "  y = float(self.convert_yunits(y))\n",
      "/Users/benjamv/opt/mambaforge/envs/CausalPy/lib/python3.11/site-packages/matplotlib/text.py:895: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
      "  y = float(self.convert_yunits(self._y))\n",
      "/Users/benjamv/opt/mambaforge/envs/CausalPy/lib/python3.11/site-packages/matplotlib/text.py:1463: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
      "  y = float(self.convert_yunits(y))\n",
      "/Users/benjamv/opt/mambaforge/envs/CausalPy/lib/python3.11/site-packages/matplotlib/text.py:895: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
      "  y = float(self.convert_yunits(self._y))\n",
      "/Users/benjamv/opt/mambaforge/envs/CausalPy/lib/python3.11/site-packages/matplotlib/text.py:1463: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
      "  y = float(self.convert_yunits(y))\n",
      "/Users/benjamv/opt/mambaforge/envs/CausalPy/lib/python3.11/site-packages/matplotlib/text.py:1463: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
      "  y = float(self.convert_yunits(y))\n",
      "/Users/benjamv/opt/mambaforge/envs/CausalPy/lib/python3.11/site-packages/matplotlib/text.py:895: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
      "  y = float(self.convert_yunits(self._y))\n",
      "/Users/benjamv/opt/mambaforge/envs/CausalPy/lib/python3.11/site-packages/matplotlib/text.py:1463: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
      "  y = float(self.convert_yunits(y))\n",
      "/Users/benjamv/opt/mambaforge/envs/CausalPy/lib/python3.11/site-packages/matplotlib/text.py:895: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
      "  y = float(self.convert_yunits(self._y))\n",
      "/Users/benjamv/opt/mambaforge/envs/CausalPy/lib/python3.11/site-packages/matplotlib/text.py:1463: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
      "  y = float(self.convert_yunits(y))\n",
      "/Users/benjamv/opt/mambaforge/envs/CausalPy/lib/python3.11/site-packages/matplotlib/text.py:1463: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
      "  y = float(self.convert_yunits(y))\n",
      "/Users/benjamv/opt/mambaforge/envs/CausalPy/lib/python3.11/site-packages/matplotlib/text.py:895: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
      "  y = float(self.convert_yunits(self._y))\n",
      "/Users/benjamv/opt/mambaforge/envs/CausalPy/lib/python3.11/site-packages/matplotlib/text.py:1463: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
      "  y = float(self.convert_yunits(y))\n",
      "/Users/benjamv/opt/mambaforge/envs/CausalPy/lib/python3.11/site-packages/matplotlib/text.py:895: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
      "  y = float(self.convert_yunits(self._y))\n",
      "/Users/benjamv/opt/mambaforge/envs/CausalPy/lib/python3.11/site-packages/matplotlib/text.py:1463: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
      "  y = float(self.convert_yunits(y))\n",
      "/Users/benjamv/opt/mambaforge/envs/CausalPy/lib/python3.11/site-packages/matplotlib/text.py:895: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
      "  y = float(self.convert_yunits(self._y))\n",
      "/Users/benjamv/opt/mambaforge/envs/CausalPy/lib/python3.11/site-packages/matplotlib/text.py:1463: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
      "  y = float(self.convert_yunits(y))\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = result.plot(round_to=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "===========================Difference in Differences============================\n",
      "Formula: y ~ 1 + group*post_treatment\n",
      "\n",
      "Results:\n",
      "Causal impact = 0.50\n",
      "Model coefficients:\n",
      "  Intercept                   \t         0\n",
      "  post_treatment[T.True]      \t     0.986\n",
      "  group                       \t     0.162\n",
      "  group:post_treatment[T.True]\t     0.504\n"
     ]
    }
   ],
   "source": [
    "result.summary(round_to=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "CausalPy",
   "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": "3.11.9"
  },
  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
    "hash": "46d31859cc45aa26a1223a391e7cf3023d69984b498bed11e66c690302b7e251"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}