Chapter06RegadjRerand.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Chapter 6: Rerandomization and Regression Adjustment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "pandas           : 2.1.1\n",
      "matplotlib       : 3.8.0\n",
      "matplotlib_inline: 0.1.6\n",
      "seaborn          : 0.12.2\n",
      "numpy            : 1.23.5\n",
      "statsmodels      : 0.13.5\n",
      "scipy            : 1.11.3\n",
      "\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import scipy as sp\n",
    "import statsmodels.api as sm\n",
    "import statsmodels.formula.api as smf\n",
    "\n",
    "# viz\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "font = {\"family\": \"IBM Plex Sans Condensed\", \"weight\": \"normal\", \"size\": 10}\n",
    "plt.rc(\"font\", **font)\n",
    "plt.rcParams[\"figure.figsize\"] = (6, 6)\n",
    "%matplotlib inline\n",
    "%config InlineBackend.figure_format = 'retina'\n",
    "\n",
    "%load_ext autoreload\n",
    "%autoreload 1\n",
    "\n",
    "%load_ext watermark\n",
    "%watermark --iversions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Regression Adjustment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_17987/2898410316.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  angrist2[\"y\"] = angrist2.GPA_year1.fillna(angrist2.GPA_year1.mean())\n"
     ]
    }
   ],
   "source": [
    "angrist = pd.read_stata(\"star.dta\")\n",
    "angrist2 = angrist.query(\"control == 1 | sfsp == 1\")\n",
    "angrist2[\"y\"] = angrist2.GPA_year1.fillna(angrist2.GPA_year1.mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "y, z, x = (\n",
    "    angrist2.y.values,\n",
    "    angrist2.sfsp.values,\n",
    "    angrist2.loc[:, [\"female\", \"gpa0\"]].values,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### unadjusted regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_17987/172137953.py:4: FutureWarning: Passing literal html to 'read_html' is deprecated and will be removed in a future version. To read from a literal string, wrap it in a 'StringIO' object.\n",
      "  unadj_res = pd.read_html(restable, header=0, index_col=0)[0].iloc[1, 0:4]\n"
     ]
    }
   ],
   "source": [
    "restable = (\n",
    "    sm.OLS(y, sm.add_constant(z)).fit(cov_type=\"HC2\").summary().tables[1].as_html()\n",
    ")\n",
    "unadj_res = pd.read_html(restable, header=0, index_col=0)[0].iloc[1, 0:4]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### adjusted (Lin 2013) regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# standardize x\n",
    "x = (x - x.mean(axis=0)) / x.std(axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_17987/3697725151.py:8: FutureWarning: Passing literal html to 'read_html' is deprecated and will be removed in a future version. To read from a literal string, wrap it in a 'StringIO' object.\n",
      "  lin_res = pd.read_html(restable, header=0, index_col=0)[0].iloc[1, 0:4]\n"
     ]
    }
   ],
   "source": [
    "restable = (\n",
    "    sm.OLS(y, sm.add_constant(np.c_[z, x, z[:, np.newaxis] * x]))\n",
    "    .fit(cov_type=\"HC2\")\n",
    "    .summary()\n",
    "    .tables[1]\n",
    "    .as_html()\n",
    ")\n",
    "lin_res = pd.read_html(restable, header=0, index_col=0)[0].iloc[1, 0:4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<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>coef</th>\n",
       "      <th>se</th>\n",
       "      <th>t</th>\n",
       "      <th>p</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>unadjusted</th>\n",
       "      <td>0.0518</td>\n",
       "      <td>0.078</td>\n",
       "      <td>0.669</td>\n",
       "      <td>0.504</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>adjusted</th>\n",
       "      <td>0.0682</td>\n",
       "      <td>0.074</td>\n",
       "      <td>0.925</td>\n",
       "      <td>0.355</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              coef     se      t      p\n",
       "unadjusted  0.0518  0.078  0.669  0.504\n",
       "adjusted    0.0682  0.074  0.925  0.355"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(\n",
    "    np.c_[unadj_res, lin_res].T,\n",
    "    columns=[\"coef\", \"se\", \"t\", \"p\"],\n",
    "    index=[\"unadjusted\", \"adjusted\"],\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Rerandomization simulation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "TBD"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def Mahalanobis2(z, x):\n",
    "    x1 = x[z == 1, :]\n",
    "    x0 = x[z == 0, :]\n",
    "    n0, n1 = x0.shape[0], x1.shape[0]\n",
    "    diff = x1.mean(axis=0) - x0.mean(axis=0)\n",
    "    covdiff = (n1 + n0) / (n1 * n0) * np.cov(x.T)\n",
    "    M = np.sum(diff * np.linalg.solve(covdiff, diff))\n",
    "    return M"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def rRem(x, n1, n0, a):\n",
    "    n = n1 + n0\n",
    "    z = np.random.choice(np.repeat([0, 1], [n0, n1]), size=n, replace=False)\n",
    "    M = Mahalanobis2(z, x)\n",
    "    while M > a:\n",
    "        z = np.random.permutation(z)\n",
    "        M = Mahalanobis2(z, x)\n",
    "    return z"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "x2 = np.c_[x, x**2]\n",
    "m1lm = sm.WLS(y, sm.add_constant(x2), weights=z).fit()\n",
    "sigma1 = m1lm.scale ** (0.5)\n",
    "\n",
    "m0lm = sm.WLS(y, sm.add_constant(x2), weights=1 - z).fit()\n",
    "sigma0 = m0lm.scale ** (0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def design_adjustment_fig(rescale):\n",
    "    a = 0.05\n",
    "    MC = 1000\n",
    "    n, n1, n0 = len(z), sum(z), sum(1 - z)\n",
    "\n",
    "    y1impute = m1lm.predict(sm.add_constant(x2)) + np.random.normal(\n",
    "        0, sigma1 * rescale, n\n",
    "    )\n",
    "    y0impute = m0lm.predict(sm.add_constant(x2)) + np.random.normal(\n",
    "        0, sigma1 * rescale, n\n",
    "    )\n",
    "    tauimpute = np.mean(y1impute - y0impute)\n",
    "\n",
    "    TauHatCRE = np.zeros(MC)\n",
    "    TauHatRegCRE = np.zeros(MC)\n",
    "    TauHatReM = np.zeros(MC)\n",
    "    TauHatRegReM = np.zeros(MC)\n",
    "\n",
    "    for i in range(MC):\n",
    "        zCRE = np.random.permutation(z)\n",
    "        yCRE = zCRE * y1impute + (1 - zCRE) * y0impute\n",
    "        TauHatCRE[i] = np.mean(yCRE[zCRE == 1]) - np.mean(yCRE[zCRE == 0])\n",
    "        TauHatRegCRE[i] = (\n",
    "            sm.OLS(yCRE, sm.add_constant(np.c_[zCRE, x, zCRE[:, np.newaxis] * x]))\n",
    "            .fit()\n",
    "            .params[1]\n",
    "        )\n",
    "\n",
    "        ZReM = rRem(x, int(n1), int(n0), a)\n",
    "        yRem = ZReM * y1impute + (1 - ZReM) * y0impute\n",
    "        TauHatReM[i] = np.mean(yRem[ZReM == 1]) - np.mean(yRem[ZReM == 0]) - tauimpute\n",
    "        TauHatRegReM[i] = (\n",
    "            sm.OLS(yRem, sm.add_constant(np.c_[ZReM, x, ZReM[:, np.newaxis] * x]))\n",
    "            .fit()\n",
    "            .params[1]\n",
    "        )\n",
    "\n",
    "    data = [\n",
    "        TauHatCRE - tauimpute,\n",
    "        TauHatRegCRE - tauimpute,\n",
    "        TauHatReM - tauimpute,\n",
    "        TauHatRegReM - tauimpute,\n",
    "    ]\n",
    "    fig, ax = plt.subplots()\n",
    "    ax.violinplot(data, showmeans=True, showmedians=True)\n",
    "    ax.set_title(\"TauHats for rescale = {}\".format(rescale))\n",
    "    ax.set_xticks([1, 2, 3, 4])\n",
    "    ax.set_xticklabels([\"TauHatCRE\", \"TauHatRegCRE\", \"TauHatReM\", \"TauHatRegReM\"])\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 526,
       "width": 535
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "design_adjustment_fig(0.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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3bqu+NO+//76ee+45OZ1OLV26VLW1taqrq9PatWt17LHHasOGDa2GhfJsQLzwTEj+M+HHbrrpJmVlZem5555TZWWlpCNTxwLPhUiWvYQ5cb8l/37ryHtwZ2vdtm2bpk6dqubmZs2cOVM7d+5UQ0ODysvLddddd0mSZsyYEQy1wpk9e7asVqseeeQRNTc3B6+5ePFiuVwuzZw5s0N1JxrhBVLaggUL9NBDD+m+++5TQUGBJCk7O1u33367Lr74YknSn/70p2SW2Mott9yi2267TW+99ZYGDx4cXI967Nixuv322yV1rOYDBw5IUqslmGLp0KFDwWUWA3Pz0HVwv0UvMOrhx/fL4sWLVVZWpn/4h3/Q0qVLNXDgQFmtVnXv3l0333yzZs+eLb/fr4ceeqjFcW+88YYkafr06Zo8ebJcLpdsNpvOO++84JzVQOPNgEQ8G77//ntVVVVJ4tnQlfBMiF6snwk/1qNHD02fPl21tbXBxn8lJSXatm2bxowZw/2ZwrjfomeG9+DO1vrQQw+ppqZG119/vR566CEVFxdLkgoKCnT33Xdr8uTJqq2t1RNPPNHm+QcNGqRLL71Uu3fv1n/9139Jkl599VUdPnxY1157bfC/KbMjvEBKO/XUU/W73/0u5LbAzR9N1/xI3X///cE5eOH+7d27N+Sx+fn5euCBB2Sz2WJac2DYW7zs2LFD//RP/6Tq6mrl5ubq5z//ecj99u7d2+b3hV+cUhf3W+QOHTqkZ555Ri+//LIktZpH+vrrr0uSpk2bFvL4q666SpL07rvvthhCun37dknSySef3OqYoUOHSlKrue7xfDb4/X5t3rxZ1157rSTphBNO0Omnnx5y388++6zNn+GvfvWruNWJ+OCZELl4PRNCmTVrljIyMvTSSy9JkpYtWyZJ+td//deOvwAkHfdb5Mz0HtyeAwcO6L777tPHH3/cqVojGZkyZ84cSWrxbLDb7Zo9e3ZUNScTPS+QtlwulySpsbEx5ud2Op3B84dz6NChqOd+x7PmaLQ1581qtWrhwoVh5x5aLJY2e25069at0/XBfLry/TZjxgzNmDEj7PYxY8a0+GXE7/fryy+/lCQNGDAg5DH9+vWTdCR4KC0t1cCBAyXFd/REewLBZDhOp1NPPvlk2H1sNlubc5Y7M0cY5sMzITHPhFB69+6ta665RosXL9Z3332n119/XcOHD9fw4cM78nKQArjfUuM9eMyYMW1unz59us4777zg/9+zZ4/KysoiqvW7775TQ0ODnE5n2POffvrp+od/+AetWLFCW7Zs0UcffaSrrroqrg1bY43wAilv/fr1WrhwoT799FPt3btXDQ0NkqT6+vq4XfN3v/tdcI5hOIWFhWGT6PLycj366KNavXq1du3apdraWhmGEZyD1hGxDAV+vJRiYMmnkSNHat68eW12fO7Vq5fKy8tjVgvMhfutNbfb3aLRZV1dnRobG1VUVKRZs2bp1ltvldX6vwMdvV5v8Ps1cuTIFttC2bdvX/AXp8AvL998802r/b7++usW+wTE6tnw42DSMIzgHN1x48Zp3rx5bY6sOuOMM/Tpp5/GpBaYB8+E1hL5TAhn9uzZeuaZZzRx4kR5vd7gX1yR2rjfWjP7e/DRMjMzWwQgNTU1am5u1sCBA3X77bfrn/7pn1rsf/T39Nhjjw15Tr/fL0ny+Xw6cOCAevfu3ebrmTNnjkaPHq0JEybIMIyU64NDeIGU9tJLLwWb2KSKjRs3atSoUTFfTjAvL0+Sgp2IO6O8vLxFyn7hhRdq9erVOuOMMzq0VBXSA/dbaA899JBuvPHG4P9/8cUXdfXVV8tisejWW29ttYJHTU1N8H9HMqWjuro6+L8nTpyohx9+WE8++aTOOecc/frXv5bD4dBnn30WHPZ52WWXtTg+Vs+GHweThmFo0KBB2rJliy688EKmhHVBPBNCS+QzIZyBAwdqwoQJeuWVVzRs2DBdcsklUbwCmBH3W2hmfw8+2muvvdZi2vUf/vAH3XnnncrJydH111/fav+jaw38ITHSWsO54IILdPbZZ+uzzz7TuHHjNGTIkHaPMRN6XiBl7d+/X9OnT1dzc7OuuuoqbdiwQTU1NcH1lefOnZvsEkO67rrrVFFRodNPP10lJSWqrKwM1lxSUtLh8w4ePFhS+PWhO2PWrFmSpCeffDLYlA9dC/db5AJLEe7Zs0fPP/98q+1HT4/49ttvg/WE+3fRRRcF9//pT3+q3//+92poaNBVV10lj8cjl8ulc889V6WlpTrzzDP1z//8zy2uF69ng8ViCc6/fvDBB1PqF2p0Hs+EyMXzmdCWwF9UU+0vq2iN+y1yZnsPbsuMGTPkdrv1xRdf6P3332+z1rq6unZrPeGEEyK6bmAkViqOyCK8QMpatWqVampq1KdPHy1dulRDhw6Vx+Np97jA8LBk/KK9c+fO4LJLy5Yt06hRo9S9e/eYnDvQJG/r1q3BodzhVFZWtrvP0caMGaOTTjpJ1dXVevTRRztVJ1IT91vkHA6Hbr75ZknSAw88IJ/P12J7Tk5OcGRTXV1d1Oe///77ddVVVyknJ0dZWVmyWq0aPHiw7rnnHv35z39u9XM544wzJElfffVVu3OS9+zZExyGHImpU6eqR48e2rFjR7ApILoGngmRi/czIZxhw4bp1ltvbfMvwUgN3G+RM9t7cFvy8vI0ZcoUSdK///u/t9p+9AogsXw2jB07VrfccovOPvvsmJ0zUQgvkLJ27dol6cjcslDz1cINnQqkmHv37m31QJPiM3IhIFCzpJDpaCTDvcIpLi7WmWeeKb/fH+yuHM6DDz6ok046SR9++GFE5z76L6yPPPJIi2Fs6Bq436Izffp0ZWZmauvWrXrttddabLNarTr11FMlHWmwFc63337b6msVFRU65ZRT5Pf7tX///uDc3Y0bN+rOO+8M2Wz33HPPVWFhofbt2xdcHi2c3//+9zrllFO0fv36SF6mPB6Ppk+fLunIL3TRNmxD6uKZEJ14PRPas2DBgnbn9MP8uN+iY6b34PbMnDlTFotFJSUl+uyzz1ps6927t4qKitqtddOmTVFd02Kx6OGHH466VjPgaYaUFehaH+jCe7Sqqqqw6ywHGuk0NzfrP//zP1ttf/fdd2NYZUtHd9oPVfeiRYs6df7f/OY3kqS5c+eGXXrq66+/1qOPPqo9e/a02fn/x6ZMmaL8/HwdOHBATz/9dKfqROrhfotO9+7dNXXqVEkK2ehs/PjxkhT2Xtq5c6fOPvtsXXfddS2+vmjRIm3cuFFz5syRw+GIqBar1RoMGGbOnBl21NUHH3yg5cuXa+fOnVH9dezmm2+Ww+HQpk2btGLFioiPQ2rjmRCdeD0T2tPWCkFIHdxv0THTe3B7Bg0aFOxJE2r0RaDWp556KuTxX3zxhU455RTdc889UV03VZ8NhBdIWWeeeaYk6e9//7v+/d//PTic6tNPP9WFF16o3NzckMfl5ubqrLPOkiTddNNNWrdunXw+n6qqqnT33XersrIybjWfeOKJwYf5rbfeGuwivHPnTl1xxRXasWNH2GMj+cvJNddco/POO0979+7Veeedp1dffTX4fdm/f7+WLFmiiy66SNXV1br22muD34dIuFyu4FJUf/zjH2PSGBSpg/ster/73e9ktVr11VdfadWqVS22XX/99SoqKlJJSYmuvvrq4Nrx9fX1eueddzRy5EgdPnw4OOUj4IcffpAkrVmzJqpaZs+erYEDB+rbb7/V8OHD9d5776mpqUmGYaisrEwPPvigxo8fL5/Ppzlz5qhv374Rn7t379664oorJIX+xQvpiWdC9OLxTEDXwP0WPTO9B7cn0Fvu7bffbjWK4p//+Z+VmZmp5557Tr///e+D38fq6mq9+OKLuvjii+Xz+brOs8EATKqgoMCQZDz77LNh9xk1apQhyZBkWCwWw+l0GpKMiy++2Fi0aJEhyXA4HEZOTk6L41avXm3YbLbgsQ6Hw5BknHDCCcZ7771nSDL69evX6nojR440JBm33357h+ufO3du8LqSDLfbbUgyCgsLjb/85S/B15KTk2N8/PHHwePq6uoMu91uSDIKCgqMgoICY9asWa2ue+DAAePss89ucQ2Xy9Xi/0+YMMFoampqcVxJSUlwe11dXcjXVF5eHvweL1mypMW2Z599tkXtbf1buXJlu98/JBb3W/T3W79+/QxJxhNPPBG2rnHjxhmSjPPPP7/VtjVr1hjZ2dmtagv8+81vfmP4/f4Wx/zpT38yLBaLIclwOp3Beyo3N9coKCgwzjjjDOP//J//Y3i93lbX27Ztm3H88ccHz2+1WoM/w8C/3/3ud62OC9zbBQUFYV/nl19+GTzH6tWrW2wL/AxsNlu7z4YNGzaEvQYSi2dCajwTAu/dI0eObPd7crRrrrnGkGTMnTs3quMQH9xvqXG/dfQ9OHDOVatWha112LBhhiTj6quvbrXt1VdfDf7cJBkej6dFrffdd1+rYwLv3ddcc03Ya4YS+Lm39d9iMjHyAint7bff1q233qri4mLZ7Xb16tVLM2fO1BtvvKErr7xSF1xwgaTWywuNHj1aq1at0jnnnCOXy6WcnBxdfvnlev/99+V0OuNa89y5c/Xwww9r8ODBysjIkMfj0WWXXaa1a9fqpz/9qW6++WZ5PB55vd4WDZZcLpf+7//9v+rRo4f27t2rvXv3hlw2qUePHlq3bp2efvppjR49Wvn5+WpublZ+fr4uvvhivfzyy1q+fHmLdaYjVVBQoEmTJkk60gQpsLb00QzDkNfrbfMfozZSE/db+8uU/Vig6/jHH3+sdevWtdh2/vnna8OGDbrxxht17LHHyufzqaioSBdeeKHeeustLVy4sNWwzksuuUS///3vJUkNDQ3Be+rgwYPau3evPv/8c/3bv/2bRo0a1apBW//+/bVhwwb98Y9/1E9/+lPl5OTI5/OpsLBQv/rVr7Rq1SrNnz8/6tcoHWkMOHr0aEnhR1/4fL52nw2h5mTDvHgmJP+ZgK6D+y3591tn3oPbExh98fLLL7calTJhwgT97W9/0+TJk3XMMceoublZxcXFGjt2rD766CPdfvvtUV0rlVkMg+5aAACYnWEYuuaaa7RmzRp9/PHHKi4ubrH90KFDWrdunSZNmqSqqiq9+eab+tWvfpWcYgEASCO8B5sDIy8AAEgBK1as0NKlS3XZZZe1+qVJkrKzszVmzJjgsmsdWZkAAAC0xnuwORBeAACQAgLLpO3fv7/N/crLyyUpqtWEAABAeLwHmwPhBQAAKSCw5N2yZcv04osvqr6+vsX2ffv26YEHHtCrr74qi8USnP8MAAA6h/dgc6DnBQAAKaCpqUkXXHBBsOmYxWJRt27dZLFYVFdXp8bGxuC+d9xxh+69995klQoAQFrhPdgcCC8AAEgR9fX1euqpp7RixQpt2rRJBw4ckM/nU2Zmpvr27auzzz5bU6dO1fnnn5/sUgEASCu8Bycf4QUAAAAAADA1el4AAAAAAABTI7wAAAAAAACmRngBAAAAAABMjfACAAAAAACYGuEFAAAAAAAwNcILAAAAAABgaoQXAAAAAADA1AgvAAAAAACAqRFeAAAAAAAAUyO8AAAAAAAApkZ4AQAAAAAATI3wAgAAAAAAmBrhBQAAAAAAMDXCCwAAAAAAYGqEFwAAAAAAwNQILwAAAAAAgKkRXgAAAAAAAFMjvAAAAAAAAKZGeAEAAAAAAEzt/wNVWl62uBTS8gAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 526,
       "width": 535
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "design_adjustment_fig(0.25)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "econometrics",
   "language": "python",
   "name": "econometrics"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}