{
 "cells": [
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    "### **Synthetic Control**\n",
    "\n",
    "\n",
    "### Background\n",
    "Unlike the Difference-in-Differences (DiD) method, synthetic control is a frequently employed technique when dealing with datasets where there is a significant imbalance between the number of control units and treated units. DiD methods typically demand a high degree of comparability between the treated and control groups to establish the critical \"parallel trend\" assumption. However, this assumption becomes challenging to fulfill when the dataset contains only a limited number, or even just a single, treated unit, often due to issues related to data collection or funding constraints. In this situation, synthetic control aims to reweight the substantial information in control group to provide another perspective to learn the conterfactuals for treated unit(s).\n",
    "\n",
    "To illustrate the basic idea of synthetic control, we suppose that there are $N$ units and $T$ time periods in total, and denote $Y_{it}$ as the outcome for unit $i$ in period $t$. Without the loss of generality, suppose the first $N_{\\text{tr}}$ units are in the treated group, which will receive treatment starting from period $T_0+1$. The rest $N_{\\text{co}} := N-N_{\\text{tr}}$ units belong to the control group, which have no possibility to be exposed to treatment at any time.\n",
    "\n",
    "\n",
    "### Algorithm \n",
    "\n",
    "There are two main steps in synthetic control methods: \n",
    "\n",
    "**Step 1:** Calculate the weights $\\hat{\\omega}_i^{\\text{sdid}}$ that align pre-exposure trends in the outcome of control units for treated units；\n",
    "\n",
    "\\begin{equation}\n",
    "    \\hat{Y}_{it} = \\hat{\\omega}_{i0} + \\sum_{j=N_{\\text{co}}+1}^{N}\\hat{\\omega}_{ij} Y_{jt}, \\qquad \\forall i\\in\\{1,\\dots, N_{\\text{tr}}\\}, \\forall t\\in \\{1,\\dots,T\\},\n",
    "\\end{equation}\n",
    "where \n",
    "\\begin{equation}\n",
    "\\hat{\\omega}_i = \\arg\\min_{\\omega} \\sum_{1\\leq t\\leq T_0} \\bigg(Y_{it} - \\omega_{i0} -\\sum_{j=1}^{N_{\\text{co}}} \\omega_{ij} Y_{jt}\\bigg)^2\n",
    "\\end{equation}\n",
    "\n",
    "\n",
    "**Step 2:** Use the weights to estimate the post-exposure conterfactuals in causal effect estimation.\n",
    "\n",
    "\n"
   ]
  },
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   "cell_type": "markdown",
   "id": "2f85b822",
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   "source": [
    "### Demo\n",
    "In the following part, we use the [Abadie-Diamond-Hainmueller California smoking data](https://www.tandfonline.com/doi/abs/10.1198/jasa.2009.ap08746?casa_token=aNT_5z3JHO0AAAAA:vyxa3Kh7WQsLZ0w5CzcvyiV-YvIJHO8kJOgYkfM14zIipcgSLxEJXN2Fr0BCpJax3xihcqbCt9S1) to illustrate how we can calculate the treatment effect on the treated via synthetic control.\n",
    "\n",
    "In this dataset, our goal aims to study the effects of Proposition 99, a large-scale tobacco\n",
    "control program that California implemented in 1988. Typically, the annual tobacco consumption was evaluated from 1970 to 2000 for a total of $N=39$ states (including California). Therefore, this dataset contains $N = 39$ units with $N_{\\text{co}} = 38$ states in the control group, and only one unit ($N_{\\text{tr}} = 1$, corresponding to the California state) starting from the $19$th period.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "33d37227",
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    {
     "data": {
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       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>California</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>...</th>\n",
       "      <th>29</th>\n",
       "      <th>30</th>\n",
       "      <th>31</th>\n",
       "      <th>32</th>\n",
       "      <th>33</th>\n",
       "      <th>34</th>\n",
       "      <th>35</th>\n",
       "      <th>36</th>\n",
       "      <th>37</th>\n",
       "      <th>38</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1970</th>\n",
       "      <td>123.0</td>\n",
       "      <td>89.8</td>\n",
       "      <td>100.3</td>\n",
       "      <td>124.8</td>\n",
       "      <td>120.0</td>\n",
       "      <td>155.0</td>\n",
       "      <td>109.9</td>\n",
       "      <td>102.4</td>\n",
       "      <td>124.8</td>\n",
       "      <td>134.6</td>\n",
       "      <td>...</td>\n",
       "      <td>103.6</td>\n",
       "      <td>92.7</td>\n",
       "      <td>99.8</td>\n",
       "      <td>106.4</td>\n",
       "      <td>65.5</td>\n",
       "      <td>122.6</td>\n",
       "      <td>124.3</td>\n",
       "      <td>114.5</td>\n",
       "      <td>106.4</td>\n",
       "      <td>132.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1971</th>\n",
       "      <td>121.0</td>\n",
       "      <td>95.4</td>\n",
       "      <td>104.1</td>\n",
       "      <td>125.5</td>\n",
       "      <td>117.6</td>\n",
       "      <td>161.1</td>\n",
       "      <td>115.7</td>\n",
       "      <td>108.5</td>\n",
       "      <td>125.6</td>\n",
       "      <td>139.3</td>\n",
       "      <td>...</td>\n",
       "      <td>115.0</td>\n",
       "      <td>96.7</td>\n",
       "      <td>106.3</td>\n",
       "      <td>108.9</td>\n",
       "      <td>67.7</td>\n",
       "      <td>124.4</td>\n",
       "      <td>128.4</td>\n",
       "      <td>111.5</td>\n",
       "      <td>105.4</td>\n",
       "      <td>131.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1972</th>\n",
       "      <td>123.5</td>\n",
       "      <td>101.1</td>\n",
       "      <td>103.9</td>\n",
       "      <td>134.3</td>\n",
       "      <td>110.8</td>\n",
       "      <td>156.3</td>\n",
       "      <td>117.0</td>\n",
       "      <td>126.1</td>\n",
       "      <td>126.6</td>\n",
       "      <td>149.2</td>\n",
       "      <td>...</td>\n",
       "      <td>118.7</td>\n",
       "      <td>103.0</td>\n",
       "      <td>111.5</td>\n",
       "      <td>108.6</td>\n",
       "      <td>71.3</td>\n",
       "      <td>138.0</td>\n",
       "      <td>137.0</td>\n",
       "      <td>117.5</td>\n",
       "      <td>108.8</td>\n",
       "      <td>140.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1973</th>\n",
       "      <td>124.4</td>\n",
       "      <td>102.9</td>\n",
       "      <td>108.0</td>\n",
       "      <td>137.9</td>\n",
       "      <td>109.3</td>\n",
       "      <td>154.7</td>\n",
       "      <td>119.8</td>\n",
       "      <td>121.8</td>\n",
       "      <td>124.4</td>\n",
       "      <td>156.0</td>\n",
       "      <td>...</td>\n",
       "      <td>125.5</td>\n",
       "      <td>103.5</td>\n",
       "      <td>109.7</td>\n",
       "      <td>110.4</td>\n",
       "      <td>72.7</td>\n",
       "      <td>146.8</td>\n",
       "      <td>143.1</td>\n",
       "      <td>116.6</td>\n",
       "      <td>109.5</td>\n",
       "      <td>141.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1974</th>\n",
       "      <td>126.7</td>\n",
       "      <td>108.2</td>\n",
       "      <td>109.7</td>\n",
       "      <td>132.8</td>\n",
       "      <td>112.4</td>\n",
       "      <td>151.3</td>\n",
       "      <td>123.7</td>\n",
       "      <td>125.6</td>\n",
       "      <td>131.9</td>\n",
       "      <td>159.6</td>\n",
       "      <td>...</td>\n",
       "      <td>129.7</td>\n",
       "      <td>108.4</td>\n",
       "      <td>114.8</td>\n",
       "      <td>114.7</td>\n",
       "      <td>75.6</td>\n",
       "      <td>151.8</td>\n",
       "      <td>149.6</td>\n",
       "      <td>119.9</td>\n",
       "      <td>111.8</td>\n",
       "      <td>145.8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 39 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      California      1      2      3      4      5      6      7      8  \\\n",
       "1970       123.0   89.8  100.3  124.8  120.0  155.0  109.9  102.4  124.8   \n",
       "1971       121.0   95.4  104.1  125.5  117.6  161.1  115.7  108.5  125.6   \n",
       "1972       123.5  101.1  103.9  134.3  110.8  156.3  117.0  126.1  126.6   \n",
       "1973       124.4  102.9  108.0  137.9  109.3  154.7  119.8  121.8  124.4   \n",
       "1974       126.7  108.2  109.7  132.8  112.4  151.3  123.7  125.6  131.9   \n",
       "\n",
       "          9  ...     29     30     31     32    33     34     35     36  \\\n",
       "1970  134.6  ...  103.6   92.7   99.8  106.4  65.5  122.6  124.3  114.5   \n",
       "1971  139.3  ...  115.0   96.7  106.3  108.9  67.7  124.4  128.4  111.5   \n",
       "1972  149.2  ...  118.7  103.0  111.5  108.6  71.3  138.0  137.0  117.5   \n",
       "1973  156.0  ...  125.5  103.5  109.7  110.4  72.7  146.8  143.1  116.6   \n",
       "1974  159.6  ...  129.7  108.4  114.8  114.7  75.6  151.8  149.6  119.9   \n",
       "\n",
       "         37     38  \n",
       "1970  106.4  132.2  \n",
       "1971  105.4  131.7  \n",
       "1972  108.8  140.0  \n",
       "1973  109.5  141.2  \n",
       "1974  111.8  145.8  \n",
       "\n",
       "[5 rows x 39 columns]"
      ]
     },
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   ],
   "source": [
    "# load the data\n",
    "# california smoking data: https://github.com/susanathey/MCPanel/blob/master/tests/examples_from_paper/california/smok_outcome.csv\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "smoke_X = pd.read_csv('smoke_covariates.csv', header = None)\n",
    "smoke_A = pd.read_csv('smoke_treatment.csv', header = None)\n",
    "smoke_R = pd.read_csv('smoke_outcome.csv', header = None)\n",
    "\n",
    "smoke_R.index = list(np.linspace(1970,2000,31).astype(int).astype(str))\n",
    "smoke_A.index = list(np.linspace(1970,2000,31).astype(int).astype(str))\n",
    "smoke_R = smoke_R.rename(columns={0: \"California\"})\n",
    "smoke_A = smoke_A.rename(columns={0: \"California\"})\n",
    "\n",
    "smoke_R.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0fb7bdbd",
   "metadata": {
    "id": "LAtbTkgLbcZU"
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   "source": [
    "Details about the dataset:\n",
    "\n",
    "*  `smoke_X`: a $7\\times 39$ dimensional matrix where each column denotes the contextual information of a specific state. The first column corresponds to California, i.e. the treated unit.\n",
    "*  `smoke_A`: a $31\\times 39$ dimensional matrix with each column denoting a specific unit trajectory among all periods. In this example, the total number of periods is $T = 31$, and the number of pre-treatment preriods is $T_0 = 18$. It is worth noting that except for the first column (which corresponds to the treated unit - California), the treatment of all other units at all periods are $0$.\n",
    "\n",
    "*  `smoke_R`: a $31\\times 39$ dimensional matrix where the $(t,i)$th element denotes the observed reward for state $i$ in time period $t$.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "7380afc9",
   "metadata": {},
   "outputs": [],
   "source": [
    "N = np.shape(smoke_R)[1]\n",
    "T = np.shape(smoke_R)[0]\n",
    "T0 = 18"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "ca2e2dc9",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-0.         -0.          0.03980357  0.07157998  0.         -0.\n",
      "  0.          0.22667637 -0.          0.          0.02990258  0.\n",
      "  0.          0.          0.00037257 -0.02313041 -0.          0.09714507\n",
      "  0.16905554  0.19878875  0.06427401  0.          0.         -0.\n",
      " -0.          0.          0.         -0.         -0.          0.\n",
      " -0.09209638  0.          0.05463154 -0.          0.          0.\n",
      "  0.          0.        ]\n",
      "38\n"
     ]
    }
   ],
   "source": [
    "# Step 1: calculate the weights to quantify the pre-exposure relationship between the treated unit and control units\n",
    "\n",
    "from sklearn import linear_model\n",
    "pre_expo_R_ctl = smoke_R.iloc[0:T0+1,1:N]\n",
    "pre_expo_R_trt = smoke_R.iloc[0:T0+1,0]\n",
    "post_expo_R_ctl = smoke_R.iloc[T0+1:T,1:N]\n",
    "post_expo_R_trt = smoke_R.iloc[T0+1:T,0]\n",
    "# fit a lasso regression to select the top units that have higher influence on the treated unit California\n",
    "\n",
    "clf = linear_model.Lasso(alpha=1)\n",
    "clf.fit(pre_expo_R_ctl, pre_expo_R_trt)\n",
    "\n",
    "print(clf.coef_)\n",
    "print(len(clf.coef_))\n",
    "#print(clf.intercept_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "f1c6d492",
   "metadata": {},
   "outputs": [
    {
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\n",
      "text/plain": [
       "<Figure size 1500x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Step 2: using the weights estimated in Step 1 to estimate the counterfactual of treated unit (California) in post-exposure period\n",
    "post_expo_R_trt_counterfactual = clf.predict(post_expo_R_ctl)\n",
    "\n",
    "# now we use a plot to show the estimated post-treatment effect in California.\n",
    "import matplotlib.pyplot as plt\n",
    "plt.figure(figsize=(15,6))\n",
    "plt.plot(smoke_R.iloc[:,0], label = \"California (observed value)\")\n",
    "plt.plot(clf.predict(smoke_R.iloc[:,1:N]), label = \"Synthetic Control (predicted value for counterfactual)\")\n",
    "plt.vlines(x=\"1988\", ymin=40, ymax=130, linestyle=\":\", label=\"treatment starting time\")\n",
    "plt.ylabel(\"per-capita cigarette sales (in packs)\")\n",
    "plt.legend();"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a1b74027",
   "metadata": {},
   "source": [
    "As we can see from the figure above, the execution of Proposition 99 can effectly reduce the pre-capita cigarette sales in California. The longer time after treatment time, the larger the treatment effect tends to be."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1098b550",
   "metadata": {
    "id": "1098b550"
   },
   "source": [
    "## References\n",
    "\n",
    "    [1] Abadie, A., Diamond, A., and Hainmueller, J. (2010), “Synthetic Control Methods for Comparative Case Studies: Estimating the Effect of California’s Tobacco Control Program,” Journal of the American Statistical Association, 105, 493–505. [2068,2069,2070,2071]\n",
    "\n",
    "    [2] Li, K. T. (2020), “Statistical Inference for Average Treatment Effects Esti-mated by Synthetic Control Methods,”Journal of the American StatisticalAssociation, 115, 2068–2083. [1716]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fc5ed54c",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "colab": {
   "collapsed_sections": [
    "1098b550"
   ],
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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",
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 },
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}