notebooks/estimating average treatment effects with double machine learning.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import networkx as nx\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import latexify\n",
    "import sympy as sp\n",
    "from scipy.special import expit, logit\n",
    "from sklearn.linear_model import RidgeCV\n",
    "from econml.dml import LinearDML\n",
    "from lightgbm import LGBMRegressor, LGBMClassifier\n",
    "from causalml.dataset import synthetic_data\n",
    "\n",
    "# Set colours for plots\n",
    "COLORS = [\n",
    "    '#00B0F0',\n",
    "    '#FF0000',\n",
    "    '#B0F000'\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_graph(input_graph, node_lookup):\n",
    "    '''\n",
    "    Function to visualise graphs.\n",
    "\n",
    "    Args:\n",
    "        input_graph (array): Adjacency matrix representing graph\n",
    "        node_lookup (dict): Dictionary containing node names.\n",
    "    '''\n",
    "    \n",
    "    graph = nx.DiGraph(input_graph)\n",
    "\n",
    "    plt.figure(figsize=(6,6))\n",
    "    nx.draw(\n",
    "    G=graph,\n",
    "    node_color='grey',\n",
    "    node_size=5000,\n",
    "    arrowsize=17,\n",
    "    with_labels=True,\n",
    "    labels=node_lookup,\n",
    "    font_color='white',\n",
    "    font_size=10,\n",
    "    pos=nx.circular_layout(graph)\n",
    "    )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Simple Problem"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create node lookup variables\n",
    "node_lookup = {0: 'Confounder',\n",
    "               1: 'Treatment',\n",
    "               2: 'Outcome'                                                                                       \n",
    " }\n",
    "\n",
    "total_nodes = len(node_lookup)\n",
    "\n",
    "# Create adjacency matrix - this is the base for our graph\n",
    "graph_actual = np.zeros((total_nodes, total_nodes))\n",
    "\n",
    "# Create graph using expert domain knowledge\n",
    "graph_actual[0, 1] = 1.0 # Confounder -> Treatment\n",
    "graph_actual[0, 2] = 1.0 # Confounder -> Outcome\n",
    "graph_actual[1, 2] = 1.0 # Treatment -> Outcome\n",
    "     \n",
    "plot_graph(input_graph=graph_actual, node_lookup=node_lookup)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x13650f2ed50>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 750x750 with 9 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "np.random.seed(123)\n",
    "\n",
    "# Create dataframe with a confounder, treatment and outcome\n",
    "df = pd.DataFrame(columns=['Confounder', 'Treatment', 'Outcome'])\n",
    "df['Confounder'] = np.random.normal(loc=100, scale=25, size=1000)\n",
    "df['Treatment'] = np.random.normal(loc=100, scale=25, size=1000) + 0.50 * df['Confounder']\n",
    "df['Outcome'] = 0.25 * df['Confounder'] + 0.75 * df['Treatment'] + np.random.normal(loc=0, scale=5, size=1000)\n",
    "\n",
    "sns.pairplot(df, corner=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Linear regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$$ \\displaystyle \\begin{array}{l} y = \\beta X + \\tau T + \\epsilon \\\\ y \\end{array} $$"
      ],
      "text/plain": [
       "<latexify.ipython_wrappers.LatexifiedFunction at 0x1c85af8cf50>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Some math symbols are converted automatically.\n",
    "@latexify.expression(use_math_symbols=True)\n",
    "def y(X, T):\n",
    "  y = beta * X + tau * T + epsilon\n",
    "  return y\n",
    "\n",
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The average treatment effect using Linear Regression is: 0.75\n"
     ]
    }
   ],
   "source": [
    "# Set target and features\n",
    "y = df['Outcome']\n",
    "X = df[['Confounder', 'Treatment']]\n",
    "\n",
    "# Train linear regression model \n",
    "model = RidgeCV()\n",
    "model = model.fit(X, y)\n",
    "\n",
    "# Extract the treatment coefficient\n",
    "ate_lr = round(model.coef_[1], 2)\n",
    "\n",
    "print(f'The average treatment effect using Linear Regression is: {ate_lr}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# FWL"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The average treatment effect using the FWL approach is: 0.75\n"
     ]
    }
   ],
   "source": [
    "# Set treatment, outcome and confounder samples\n",
    "treatment = df['Treatment'].to_numpy().reshape(-1,1)\n",
    "outcome = df['Outcome'].to_numpy().reshape(-1,1)\n",
    "confounder = df['Confounder'].to_numpy().reshape(-1,1)\n",
    "\n",
    "# Train treatment model and calculate residuals\n",
    "treatment_model = RidgeCV()\n",
    "treatment_model = treatment_model.fit(confounder, treatment)\n",
    "treatment_pred = treatment_model.predict(confounder)\n",
    "treatment_residuals = treatment - treatment_pred\n",
    "\n",
    "# Train outcome model and calculate residuals\n",
    "outcome_model = RidgeCV()\n",
    "outcome_model = outcome_model.fit(confounder, outcome)\n",
    "outcome_pred = outcome_model.predict(confounder)\n",
    "outcome_residuals = outcome - outcome_pred\n",
    "\n",
    "# Train residual model and calculate average treatment effect\n",
    "final_model = RidgeCV()\n",
    "final_model = final_model.fit(treatment_residuals, outcome_residuals)\n",
    "ate_fwl = round(final_model.coef_[0][0], 2)\n",
    "\n",
    "print(f'The average treatment effect using the FWL approach is: {ate_fwl}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# DML"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The average treatment effect using the DML is: 0.75\n"
     ]
    }
   ],
   "source": [
    "# Train DML model\n",
    "dml = LinearDML(discrete_treatment=False)\n",
    "dml.fit(df['Outcome'].to_numpy().reshape(-1,1), T=df['Treatment'].to_numpy().reshape(-1,1), X=None, W=df['Confounder'].to_numpy().reshape(-1,1))\n",
    "\n",
    "# Calculate average treatment effect\n",
    "ate_dml = round(dml.ate()[0], 2)\n",
    "\n",
    "print(f'The average treatment effect using the DML is: {ate_dml}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Complex Problem"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "|Variable|Description|\n",
    "|-|-|\n",
    "|x01|days_since_last_order|\n",
    "|x02|order_count_last_07_days|\n",
    "|x03|order_count_last_30_days|\n",
    "|x04|order_count_last_180_days|\n",
    "|x05|order_value_last_07_days|\n",
    "|x06|order_value_last_30_days|\n",
    "|x07|order_value_last_180_days|\n",
    "|x08|age|\n",
    "|x09|gender|\n",
    "|x10|city|\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.random.seed(123)\n",
    "\n",
    "# Set number of observations\n",
    "n=100000\n",
    "\n",
    "# Set number of features\n",
    "p=10\n",
    "\n",
    "# Create features\n",
    "X = np.random.uniform(size=n * p).reshape((n, -1))\n",
    "\n",
    "# Nuisance parameters\n",
    "b = (\n",
    "    np.sin(np.pi * X[:, 0] * X[:, 1])\n",
    "    + 2 * (X[:, 2] - 0.5) ** 2\n",
    "    + X[:, 3]\n",
    "    + 0.5 * X[:, 4]\n",
    "    + X[:, 5] * X[:, 6]\n",
    "    + X[:, 7] ** 3\n",
    "    + np.sin(np.pi * X[:, 8] * X[:, 9])\n",
    ")\n",
    "\n",
    "# Create binary treatment\n",
    "T = np.random.binomial(1, expit(b))\n",
    "\n",
    "# Set treatment effect\n",
    "tau = 0.75\n",
    "\n",
    "# Calculate outcome\n",
    "y = b + T * tau + np.random.normal(size=n)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$$ \\displaystyle \\begin{array}{l} y = \\sin \\mathopen{}\\left( \\pi \\cdot \\mathrm{x1} \\cdot \\mathrm{x2} \\mathclose{}\\right) + 2 \\mathopen{}\\left( \\mathrm{x3} - 0.5 \\mathclose{}\\right)^{2} + \\mathrm{x4} + 0.5 \\mathrm{x5} + \\mathrm{x6} \\cdot \\mathrm{x7} + \\mathrm{x8}^{3} + \\sin \\mathopen{}\\left( \\pi \\cdot \\mathrm{x9} \\cdot \\mathrm{x10} \\mathclose{}\\right) + \\tau T + \\epsilon \\\\ y \\end{array} $$"
      ],
      "text/plain": [
       "<latexify.ipython_wrappers.LatexifiedFunction at 0x25a0ee29750>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Some math symbols are converted automatically.\n",
    "@latexify.expression(use_math_symbols=True)\n",
    "def y():\n",
    "  y = sin(pi * x1 * x2) + 2 * (x3 - 0.5) **2 + x4 + 0.5 * x5 + x6 * x7 + x8 **3 + sin(pi * x9 * x10) + tau * T + epsilon\n",
    "  return y\n",
    "\n",
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[LightGBM] [Info] Number of positive: 45387, number of negative: 4613\n",
      "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.006880 seconds.\n",
      "You can set `force_col_wise=true` to remove the overhead.\n",
      "[LightGBM] [Info] Total Bins 2550\n",
      "[LightGBM] [Info] Number of data points in the train set: 50000, number of used features: 10\n",
      "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.907740 -> initscore=2.286347\n",
      "[LightGBM] [Info] Start training from score 2.286347\n",
      "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001653 seconds.\n",
      "You can set `force_col_wise=true` to remove the overhead.\n",
      "[LightGBM] [Info] Total Bins 2550\n",
      "[LightGBM] [Info] Number of data points in the train set: 50000, number of used features: 10\n",
      "[LightGBM] [Info] Start training from score 3.146676\n",
      "[LightGBM] [Info] Number of positive: 45387, number of negative: 4613\n",
      "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001752 seconds.\n",
      "You can set `force_col_wise=true` to remove the overhead.\n",
      "[LightGBM] [Info] Total Bins 2550\n",
      "[LightGBM] [Info] Number of data points in the train set: 50000, number of used features: 10\n",
      "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.907740 -> initscore=2.286347\n",
      "[LightGBM] [Info] Start training from score 2.286347\n",
      "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001747 seconds.\n",
      "You can set `force_col_wise=true` to remove the overhead.\n",
      "[LightGBM] [Info] Total Bins 2550\n",
      "[LightGBM] [Info] Number of data points in the train set: 50000, number of used features: 10\n",
      "[LightGBM] [Info] Start training from score 3.160686\n",
      "The average treatment effect using the DML is: 0.74\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(123)\n",
    "\n",
    "# Train DML model using flexible stage 1 models\n",
    "dml = LinearDML(model_y=LGBMRegressor(), model_t=LGBMClassifier(), discrete_treatment=True)\n",
    "dml.fit(y, T=T, X=None, W=X)\n",
    "\n",
    "# Calculate average treatment effect\n",
    "ate_dml = round(dml.ate(), 2)\n",
    "\n",
    "print(f'The average treatment effect using the DML is: {ate_dml}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The average treatment effect using the DML is: 0.74\n"
     ]
    }
   ],
   "source": [
    "print(f'The average treatment effect using the DML is: {ate_dml}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The average treatment effect using Linear Regression is: 0.85\n"
     ]
    }
   ],
   "source": [
    "# Append features and treatment\n",
    "X_T = np.append(X, T.reshape(-1, 1), axis=1)\n",
    "\n",
    "# Train linear regression model \n",
    "model = RidgeCV()\n",
    "model = model.fit(X_T, y)\n",
    "y_pred = model.predict(X_T)\n",
    "\n",
    "# Extract the treatment coefficient\n",
    "ate_lr = round(model.coef_[-1], 2)\n",
    "\n",
    "print(f'The average treatment effect using Linear Regression is: {ate_lr}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Iby/zofT0dOGX8qeffgpDQ0OZ+T///DMAoG/fvvjyyy8VlqGjo4NVq1bBwMAAmZmZOHjwoNLtadKkCb755hul84HCngVVg88bNmyI6dOnAwDCw8PlxspcuXIFN27cAACMHTtW5gbbUvXq1cMPP/ygsh6bNm1CRkYGzM3NsWbNGujr6ytcbvz48UKPzfbt21WWWRGK26fq+Ay1tLTQpEkTlfWYP38+6tatC4lEIoxhVYehQ4fi008/lZvu4OCALl26ACgcszl58mR07NhRbrm+ffvC2toaAHDhwgW5+evWrUNubi7s7e2xdOlSaGsrPtwtWrQIlpaWKCgoUHlBloWFBX766SeF5UyePFlo2+W9UXhpLVu2DN26dSv29fz58wqthzr29++//46srCwAwKpVq9C2bVul79eoUSO11V2R3bt3IykpCQDw9ddfo3fv3kqXrVOnDoyMjIT/Jycn4/DhwwCAbt26YdKkSXLraGtr4+effxZ6tKQ9+sp88803aNy4sdz0UaNGCcf3V69eYcWKFQpvLzZu3Djh34r+Xorq27cvPvvsM7np+vr6WLdundBjqajO5T3Of+jbb7+Fra2tymWUKXrWxNnZWelyWlpacmNGjx49imfPngEApk+fjk6dOsmtV6dOHfzyyy8ACsdC/vnnnyrrs379eoVjU11cXIRjXGUfPxgCq4BEIsHu3bsBFJ6Gs7S0lJmvp6cnnF4LCwtDenq6zPw6derAzc0NAHDgwAHk5OTIvcfBgweFK/o+PBWckZEhDM4u7skkZmZmaN26NQAgJiZG6XKenp6lvnIwIyMDjx8/xp07d3D79m3cvn0bNWrUEOZ9OOBceq8yAPD19VVabr9+/VCnTh2l86VBpk+fPsL7KSMNmqq2vaKo2qcV8RkChQeyFy9e4P79+8JncvfuXeGU382bN0u7GUpJ27gi0uERxS3Xpk0bAMDjx4/l5h07dgxA4YUYygIJUPj3Jj3Aq9o/gwYNkvsxJWViYoJmzZoprYsYqGN/h4eHAyg8xTxkyJCKqWgJSetiYGAgE6BK4ty5c8jLywMAjBkzRulypqamGDp0KIDCix8UXWQDFIYU6XIfMjIyEn7ImZmZCd8NH7Kzs0OtWrUAFN9GR44cqXSenZ0dXFxcABRup6qLRIDSH+eL0tfXV7rdJdGgQQPh36W9+OrMmTPCv1V9ht26dRNO3xZd50P29vZwcnJSOr9du3YAKv/4wTGBVeDcuXPC1VGKxupJp//xxx/Izs5GaGgoxo4dKzc/PDwcaWlpOHHihFyPyp49ewAUHky7d+8uMy8+Pl64QezUqVNL/Pipor+qPlTSO/Q/ffoUa9euRXh4eLFXiL1+/VrmnnvSK+B0dHRUvp90vqKrrfLz84XexODg4BLfikfVtlcUVduozs9QIpFgz5492LZtG65evYr//e9/StdXdoV5WUhDkyJF70VXkuWkvUdST58+Fa72W7FihcxVsKqo+pxVjV0CIPzC/7AuFW39+vUqv7Qrgzr2d15eHm7fvg0A+Pjjj1UGycogvfrUwcEBJiYmpVq36NW6inqQiurYsaMwtu727dsKe73q1q2rcgyc9O+gSZMmSseIS5fLzMwsto126NCh2Plnz54Vxg5/eDahPMf5opo2bSrTw1paXbp0QZMmTZCQkIAFCxZgz5498PDwQLdu3dC+fXulP+qA//sMLS0ti+117tixI+7du4fExERkZmYKYbuo4sb5VdXxgyGwCkiDR40aNWRuHVFUx44d0bRpUzx8+BDBwcFyIfCTTz6BiYkJMjIysHfvXpkQ+OzZM2FAt5eXl9zBVNWl8KooewoGAIVd3B+KiIjA559/rrKcoj4MI2n//yKXWrVqKT2FK6XsaStv3rwRfqGXhqpgVFFU7VN1fYbZ2dkYPXo0IiIiSrS+OveDqoN70TarqrdWupw0EEtVRBsv7stIWV3EQB37OzU1VehVsrCwUEu9ykO6TWWpy5s3b4R/F/fkp6LlF12vqJK2veKWkwbE4tpocU8sKjo/NTVVJgSW9zhfVHlu6wMU9jrv2rULY8eOxe3btxEXF4e4uDgAhT28nTt3ho+PD3x9feW+U6SfRUme3PXhZ6goBJb0MyyuZ1XdGAIr2du3b4WxIu/evSvRuJZLly7h4cOHaNq0qTDN0NAQAwcOxI4dO3D8+HFkZGQIv1ZDQkKEhuTj4yNXXtEDwIoVK+R6CpUpyZexMq9fv8YXX3yBd+/ewdjYGNOmTRPuzWdiYiL8AZ49e1Y4vVncWJGyKLrtw4cPh7+/v9rfQ11U7VN1fYarVq0SAqCzszMmTJiAjz76CPXr14eRkZFQh08++QQXLlyokM+kIhTdP7NmzVL4d6BIcT8uSDHub+VU9cxpqrLWWd3HeXX0Brdo0QJRUVGIiIhAWFgYzp8/j/v37+P9+/c4d+4czp07hzVr1mDPnj0Kx0dXx8+vNBgCK9mhQ4fK1N0bHBws3HpBatiwYdixYweys7Nx6NAhjBo1CsD/nQpu3bq1wjEIRZ8hamRkVCmPLjt48KAwtnH79u3o2bOnwuXSVDwDUvqrMDMzEzk5OSq/QJT1TNSpUwdaWlqQSCSQSCTV9rFt6vgMJRIJtm3bBgDo2rUrDh8+rPSgq+pz0URF94+Ojk61/ZyrC3Xs79q1a0NbWxsFBQVISUlRZ/XKpG7dunj27FmZ6lL01O3Lly9Vnk4uWn5JbntSGf7++2+VHRQvX74U/l10/LU6jvMVQVtbG/369UO/fv0AFNb/zJkz2Lx5My5cuIAHDx7Az89PZgiR9LMouq3KaOJnWFK8MKSSSU8F161bV7hHl6qXNMTt2bNH7hdT9+7d0bBhQ2E+UDimRPokkWHDhimsg4ODg/DrprirxNRFOr6idu3aSg8MAISuekWk9/4rOq5PEVXz9fT0hIskLl68WG16tj6kjs/wzZs3wsFryJAhSgNgVlYW7t+/r7QcTfylbGtrK3zxVlYbV0UT95E6qWN/6+npCeHx4sWLZTotps79LH0Szo0bN5CRkVGqdaXHGKDwrgaqXL16Vfi3pvxYKVonRWJjYwEANWvWlBnDqI7jfGWoV68ehg0bhrCwMOFCmuvXr8s89Uf6Gb548UK4SlgZ6f6ytrZWeCpYkzEEVqLExEScO3cOQOFtW7y8vIp9SS/Tf/r0qdzjlqQ3LwWAqKgovHjxQgiDWlpacreekTI3N8fHH38MoPCXW0XfOgL4v9NF79+/V3pwf/funXDVtCKurq7Cv3ft2qV0uePHj6u8gEF6M+OnT59WyaPh1EEdn2HRsZGqxu9s3bpV5TjKooOrFV2pXhV0dHTQv39/AIU3QZY+TrCqaOI+Uid17W9pGS9evEBoaGip15fuZ3XsY2ldpDfVLo3u3bsLV/arur1URkaGsJ1NmjQp861Q1G3nzp1K5z158kT4HuvevbvMDa7VcZyvTFpaWujRo4fw/9evXwv/7tWrl/BvVZ/hxYsXcffuXbl1qguGwEq0e/duoeepuNt6SA0aNEj4davoSlZpb19BQQH27t2LkJAQAIWn96T3UFPkq6++AlD4Bzl69GiZxv+h/Px87N69u9hfQ6pIx1q8e/dO4cE9Pz8fM2bMwIsXL5SW0blzZ+GWIFu2bJF7wgFQeBp44cKFKusyadIkoddC+tQKVc6fP1/i551WpvJ+hubm5sJVhSEhIcIthYqKjY0t9r6LderUEU7Nq3o2aWWbNWsWdHV1IZFIhCcgKCORSHDs2DG13gKnqKIDxzVpH6mTOvb3xIkTYWxsDKCwfV+/fl1pGdJ7+BUl3c8vX75EZmZmGbbi/wwfPlw407JixQqcPn1a6bKpqakyFzg0aNBAuOjv3LlzCp+sIZFIMGfOHOEH68SJE8tVX3U6fvy40KFQVE5ODmbMmCGEvQ+fQKSO47w6nT9/Hg8fPlQ6X/qcY6AwENrY2AjzPDw8hM9/zZo1Cr8n0tLSMHPmTGH9L774Qn2VryQcE1iJpL1XtWvXlvn1oUrDhg3RsWNHXL58GYcOHcKPP/6ImjVrCvMdHBxgb2+P27dv46effhLGYyi79YyUm5sbpk2bhnXr1uHq1avo3Lkz/Pz84OzsjLp16+J///sfnjx5gpiYGBw+fBgpKSk4f/688EdRWkOHDkVgYCDev3+PqVOn4saNG+jVqxdMTExw584dbNy4EdeuXUOXLl1w8eJFpeWsWrUKHh4eyM3NhaenJ6ZMmSL32LiUlBQ4Ojrixo0bCk8PmZubY8OGDUJw6tu3L4YNG4Z+/frB2toa+fn5SE5ORlxcHI4ePYo7d+5g5cqVwr2xNEV5P0NtbW0MGzYMf/zxB27duoX+/ftj6tSpaNq0KTIyMnDixAls2rQJNWvWRIMGDfDgwQOF9dDV1UX79u1x8eJFbN++HU5OTnB0dBRuGFu7du0qGSfTunVr/PDDD5g3bx4ePXoEFxcXjBo1Cr1794aFhQVycnLw/Plz4W/r6dOn2LVrl8w9CtXF2toaDRs2xLNnz7B27VpYWVmhefPmQi9KvXr1ynwa6cWLF8KtVVSpUaOG0ltxqIM69nf9+vXx888/Y8KECUhNTUXfvn3lHht37949nDx5EmFhYXLjtaS94wUFBZg9ezYmTpwoM16xuBujF2VgYICNGzdi8ODByM7Ohre3N7y9vTFw4EBYW1sjLy8PCQkJOHPmDA4dOoTz58/L9OT98MMPOHv2LFJTUzFnzhzExMTAx8cHdevWxePHj/H7778Lp847d+6s8rGcla19+/aYNGkSzp8/j6FDh8LU1BT37t3D2rVrhaE2gwcPlrsnobqO8+py9uxZ/Pjjj+jSpQv69u0LBwcHmJubIycnB48fP8a2bdtkzs4V/bGmp6eHf/3rX/Dx8cHbt2/h4eGByZMnw83NTeaxcdJb4EyfPl3lfQA1FUNgJYmJiRG+RAcMGFCqGysPGjQIly9fRlZWFg4fPix3o+Thw4fj22+/FQKggYFBiXoaly1bhjp16mD58uV4/fo1Vq1ahVWrVilcVl9fX+U9lYrTsGFD/PTTT5gxYways7Pxyy+/CHdal/L09MTnn3+usu5du3bFunXr4O/vj+zsbKxevRqrV68W5uvq6mL16tWIjo7GjRs3lNbZw8MDe/bswZdffonXr19j+/btKrv8NXWcR3k/w0WLFuHixYu4ceMG4uLi5H7J1q5dG1u3bsUPP/ygNAQChb1Avr6+SE1NlStj/vz5WLBgQRm3sHwmTpyImjVrYt68ecjKysJvv/0m9/xjKW1t7WJvHl4es2fPxpw5c/DkyROMGDFCZl557vW3bNkyLFu2rNjlnJ2dlT4jVV3Usb+lVxbPnDkTb9++lXm+cXF69OiBTp064fLly9i7d6/cE5VKe0GCs7Mz9u3bh3HjxuH169fYs2ePwh4yRSwtLXHo0CEMHz4cz549w65duxQOY+natSuCg4OVPje4Kvz73//GkCFDsGXLFoXPTe/Rowc2bNggN11dx3l1KigowPnz5xWeOZJydnbG2rVr5aa7u7tj48aNmD59Ot6+fav0+DphwgQsXbpUndWuNDwdXEmKnsotbeMvuryiU8Le3t4yg/r79u1b4vsrzZ49G1evXsWcOXPQoUMH1KlTB7q6ujA2NkazZs0wePBg/Pzzz7hz506pfkUrMmrUKBw7dgweHh4wNzeHnp4eGjRoAHd3d2zevBn//ve/S3RLgBEjRuDMmTMYNmwYLC0toa+vDysrKwwdOhTh4eEYM2aMcCpI1VV57u7uuH79OoKCgtCrVy80aNBACEqNGjWCm5sblixZgsuXLyt8hJKmKM9naGpqiuPHj+Obb76Bvb09DA0NYWxsjJYtW2L69OmIiopS+bglqX79+uHgwYMYMGAALC0tVT42qrKNHDkS8fHxWLRoEbp16wZzc3Po6uoKPWP9+/fHDz/8gPj4+BL30JfF+PHjsXXrVvTu3Rv16tUr9RN2qgt17G8fHx9cu3YN8+bNQ4cOHVC7dm3o6OjAxMQEbdu2xZQpUxQ+nUFbWxv79+/H3Llz4eDgAGNj43JfLOLq6opr167hu+++Q7du3VC3bl3h78ve3h5+fn44dOiQwvF8Dg4OiImJQWBgILp06YLatWtDT08PFhYW6Nu3L/744w+EhYWV+3546mZnZ4e//voLX331FVq3bo2aNWuiVq1a6Ny5M3755RccOHBA6Q8mdR3n1WHGjBnYtm0bvvjiC3Tu3BnW1tYwNDSEgYEBGjVqhE8//RRbtmzBkSNHlH4GPj4+uHLlCqZNmwZ7e3vUqlULBgYGsLa2xvDhw3H8+HH8+OOPVX5z87LSSktLq56XRxKp0K5dOzx69AjDhg0r9pmcRERiFxQUJDzppbrdEorKrnpGVyIVYmNjhcH30odyExERkSyGQKp2it7L6UOpqamYMWMGgMIxcJ6enpVVLSIiomrlnzkohf7RvL29YWlpiYEDBwoPd09LS0NMTAz+/PNPJCcnAygcK1eS5z4SERGJEUMgVTsSiQTR0dGIjo5Wuoyfn59wHz0iIiKSxxBI1c769etx7NgxREdHIyUlBa9evYKOjg7q16+PLl264PPPP0fXrl2ruppEREQajVcHExEREYkQLwwhIiIiEiGGQCIiIiIRYggkIiIiEiGGQCIiIiIRYggkIiIiEiGGQCIiIiIRYggkIiIiEiGGQCIiIiIRYggkIiIiEiGGQCIiIiIRYggkIiIiEiGGQCIiIiIRYggkIiIiEiGGQCIiIiIRYggkIiIiEiGGQCIiIiIRYggkIiIiEiGGQCIiIiIRYggkIiIiEiHdqq4AERFRddTm15iqrgL9w9ya0rlS3489gUREREQixBBIREREJEIMgUREREQixBBIREREJEIMgUREREQixBBIREREJEIMgUREREQixBBIREREJEIMgUREREQixBBIREREJEIMgUREREQixBBIREREJEIMgUREREQixBBIREREJEIMgUREREQiVG1CYGxsLHx8fGBjYwMrKyu4u7sjNDS0VGW8ePEC8+fPx8cffwwrKys0b94c/fv3x65du5Cfn19BNSciIiLSPLpVXYGSiIyMhJeXFwwNDeHp6QljY2McOnQIfn5+SEpKwvTp04st4/Hjx3Bzc0Nqairc3NzQv39/ZGZm4ujRo5g0aRIiIyPx66+/VsLWEBEREVU9rbS0NElVV0KVvLw8dOrUCc+fP0dERAScnJwAAOnp6XBzc8PTp09x5coV2NjYqCxnzpw52LRpE4KCgjB58mRhelpaGlxcXJCUlIT4+PhiyyEiIgKANr/GVHUV6B/m1pTOlfp+Gn86ODIyEo8ePYK3t7cQAAHA1NQUs2fPRk5ODoKDg4st5/HjxwCAvn37ykw3MzND165dAQCpqanqqzgRERGRBtP4EBgVFQUA6N27t9w8Nzc3AEB0dHSx5bRu3RoAcOLECZnpaWlpuHjxIiwsLNCyZcvyVpeIiIioWtD4MYEPHz4EADRt2lRunoWFBYyNjZGQkFBsOTNmzEB4eDgWLlyIU6dOoU2bNsKYQCMjI2zfvh1GRkbFlpOdnV36jSAiIiIqRnkzhqGhYamW1/gQmJGRAQAwMTFROL9WrVrCMqrUr18fERERmDhxIiIiInDy5EkAgJGREfz8/ODg4FCi+jx//pxXEhMREZHaJSYmlnldHR0dNGnSpFTraHwIVJeEhAT4+vqiZs2aOHbsGBwdHZGeno49e/Zg2bJlOH36NI4dOwYdHR2V5VhZWVVSjYmISLO9qeoK0D+MtbV1pb6fxodAaQ+gst6+zMxMmJmZFVvOlClTkJiYiGvXrsHCwgIAYGxsjFmzZuHvv//Ghg0bsG/fPgwbNkxlOaXtaiUiIiIqicrOGBp/YYh0LKB0bGBRKSkpyMrKKrb7MzMzExcvXkSLFi2EAFhU9+7dAQDx8fFqqDERERGR5tP4EOjs7AwAOH36tNy8U6dOySyjTG5uLgDg9evXCue/evUKAGBgYFDmehIRERFVJxofAl1dXWFnZ4eQkBCZnrr09HSsXr0a+vr68PX1FaYnJyfj3r17SE9PF6bVqVMHzZs3R1JSErZu3SpTflpaGtatWwfg/3oEiYiIiP7pNP6JIYDyx8YlJiYiMDBQ5rFxkydPRnBwMNavX4+RI0cK0yMiIvDZZ58hLy8Prq6ucHJyQlpaGo4dO4ZXr15h0KBBcgGRiIhIGT4xhNStsp8YovEXhgBAjx49EB4ejqCgIISGhiI3Nxf29vYICAiAp6dnicro06cPTpw4gTVr1uDixYuIjo6GoaEhWrRogXnz5mH8+PEVvBVEREREmqNa9AQSERFpGvYEkrrx2cFEREREVOEYAomIiIhEiCGQiIiISIQYAomIiIhEiCGQiIiISIQYAomIiIhEiCGQiIiISIQYAomIiIhEiCGQiIiISIQYAomIiIhEiCGQiIiISIQYAomIiIhESLeqKyBGfOg4qVNlP3CciIj+GdgTSERERCRCDIFEREREIsQQSERERCRCDIFEREREIsQLQ4ioQvACKFInXgBFpH7sCSQiIiISIYZAIiIiIhFiCCQiIiISIYZAIiIiIhFiCCQiIiISIYZAIiIiIhFiCCQiIiISIYZAIiIiIhFiCCQiIiISIYZAIiIiIhFiCCQiIiISoWoTAmNjY+Hj4wMbGxtYWVnB3d0doaGhJV7f0dERZmZmKl/nz5+vwC0gIiIi0hy6VV2BkoiMjISXlxcMDQ3h6ekJY2NjHDp0CH5+fkhKSsL06dOLLWPy5MlIT0+Xm56amoo//vgDZmZmaN++fUVUn4iIiEjjaHwIzMvLg7+/P7S1tXH06FE4OTkBAObNmwc3NzcEBgZi8ODBsLGxUVnOlClTFE5fu3YtAGDYsGEwNDRUb+WJiIiINJTGnw6OjIzEo0eP4O3tLQRAADA1NcXs2bORk5OD4ODgMpe/fft2AMDo0aPLXVciIiKi6kLjewKjoqIAAL1795ab5+bmBgCIjo4uU9mXLl3C3bt30a5dOzg6OpZonezs7DK9F1FFYZskMWA7JzEobzsv7RlNjQ+BDx8+BAA0bdpUbp6FhQWMjY2RkJBQprK3bdsGABgzZkyJ13n+/Dny8/PL9H5EFSExMbGqq0BU4djOSQzK0851dHTQpEmTUq2j8SEwIyMDAGBiYqJwfq1atYRlSiMrKwsHDhxAjRo14OXlVeL1rKysSv1e8t6ooQyiQtbW1lVdBSXYzkl9NLOds42TelV2O9f4EFhR9u/fj6ysLHz22WdKA6YivHiENA3bJIkB2zmJQWW3c42/MEQa0JT19mVmZpYqxElJLwgpzalgIiIion8KjQ+B0rGA0rGBRaWkpCArK6vU58D/+9//IiYmBi1atEDXrl3VUk8iIiKi6kTjQ6CzszMA4PTp03LzTp06JbNMSUkvCOFtYYiIiEisND4Eurq6ws7ODiEhIYiPjxemp6enY/Xq1dDX14evr68wPTk5Gffu3VP4dBAAyM3Nxe7du6GnpyezHhEREZGYaHwI1NXVxZo1a1BQUAAPDw/4+/vjm2++gYuLCx48eIDFixfD1tZWWD4gIACdO3fGkSNHFJYXFhaGV69eoX///qhXr15lbQYRERGRRqkWVwf36NED4eHhCAoKQmhoKHJzc2Fvb4+AgAB4enqWqixeEEJEREQEaKWlpUmquhJi0+bXmKquAv2D3JrSuaqroBDbOamTJrZztnFSt8pu5xp/OpiIiIiI1I8hkIiIiEiEGAKJiIiIRIghkIiIiEiEGAKJiIiIRIghkIiIiEiEGAKJiIiIRIghkIiIiEiEGAKJiIiIRIghkIiIiEiEGAKJiIiIRIghkIiIiEiEGAKJiIiIRIghkIiIiEiEGAKJiIiIRIghkIiIiEiEGAKJiIiIRIghkIiIiEiEGAKJiIiIRIghkIiIiEiEGAKJiIiIRIghkIiIiEiEGAKJiIiIRIghkIiIiEiEGAKJiIiIRIghkIiIiEiEGAKJiIiIRIghkIiIiEiEqk0IjI2NhY+PD2xsbGBlZQV3d3eEhoaWupyXL19iwYIFaN++PSwsLNC4cWP06dMHmzZtqoBaExEREWkm3aquQElERkbCy8sLhoaG8PT0hLGxMQ4dOgQ/Pz8kJSVh+vTpJSonPj4enp6eSEtLQ9++fTF48GBkZWXh3r17CA8Px/jx4yt4S4iIiIg0g8aHwLy8PPj7+0NbWxtHjx6Fk5MTAGDevHlwc3NDYGAgBg8eDBsbG5XlZGRkYMSIEQCAv/76Cw4ODnLvQ0RERCQWGn86ODIyEo8ePYK3t7cQAAHA1NQUs2fPRk5ODoKDg4stZ9OmTUhKSsK3334rFwABQFdX4/MwERERkdpofPKJiooCAPTu3VtunpubGwAgOjq62HL2798PLS0tDBo0CPfv38fp06eRnZ2N5s2bw93dHfr6+uqtOBEREZEG0/gQ+PDhQwBA06ZN5eZZWFjA2NgYCQkJKsvIycnB7du3YW5ujo0bNyIoKAgFBQXCfDs7O+zYsQNt2rQptj7Z2dml3AKiisU2SWLAdk5iUN52bmhoWKrlNT4EZmRkAABMTEwUzq9Vq5awjDJv3rxBfn4+UlNTsXLlSgQEBMDX1xe5ubnYvHkzVq1aBV9fX1y+fLnYHfj8+XPk5+eXbWOIKkBiYmJVV4GowrGdkxiUp53r6OigSZMmpVpH40OgOkh7/fLz8zFhwgSZq4m/+eYbPHjwAKGhoTh48CCGDx+usiwrKys11OiNGsogKmRtbV3VVVCC7ZzURzPbOds4qVdlt3OND4HSHkBlvX2ZmZkwMzMrURkA8Mknn8jN/+STTxAaGoq4uLhiQ2Bpu1qJKhrbJIkB2zmJQWW3c42/Olg6FlA6NrColJQUZGVlFdv9WbNmTaEHz9TUVG6+dBrHnBAREZFYaHwIdHZ2BgCcPn1abt6pU6dkllGle/fuAIC7d+/KzZNOK+5eg0RERET/FBofAl1dXWFnZ4eQkBDEx8cL09PT07F69Wro6+vD19dXmJ6cnIx79+4hPT1dppxx48YBAH755RekpaUJ01NSUvDbb79BW1sbgwYNqtiNISIiItIQGh8CdXV1sWbNGhQUFMDDwwP+/v745ptv4OLiggcPHmDx4sWwtbUVlg8ICEDnzp1x5MgRmXI+/vhjTJ06FXfu3IGLiwvmzp0Lf39/uLi44Pnz51i0aBGaNWtW2ZtHREREVCU0/sIQAOjRowfCw8MRFBSE0NBQ5Obmwt7eHgEBAfD09CxxOd9//z3s7e3x559/YufOndDS0oKTkxNWr16NgQMHVuAWEBEREWkWrbS0NElVV0Js2vwaU9VVoH+QW1M6V3UVFGI7J3XSxHbONk7qVtntXONPBxMRERGR+jEEEhEREYkQQyARERGRCDEEEhEREYkQQyARERGRCDEEEhEREYkQQyARERGRCDEEEhEREYkQQyARERGRCDEEEhEREYkQQyARERGRCDEEEhEREYkQQyARERGRCDEEEhEREYkQQyARERGRCDEEEhEREYkQQyARERGRCDEEEhEREYkQQyARERGRCDEEEhEREYkQQyARERGRCDEEEhEREYkQQyARERGRCDEEEhEREYkQQyARERGRCDEEEhEREYkQQyARERGRCDEEEhEREYkQQyARERGRCFWbEBgbGwsfHx/Y2NjAysoK7u7uCA0NLfH6O3bsgJmZmdLXuXPnKrD2RERERJpFt6orUBKRkZHw8vKCoaEhPD09YWxsjEOHDsHPzw9JSUmYPn16icsaMGAAHB0d5abb2Nios8pEREREGk3jQ2BeXh78/f2hra2No0ePwsnJCQAwb948uLm5ITAwEIMHDy5xiPPw8MDIkSMrsspEREREGk/jTwdHRkbi0aNH8Pb2FgIgAJiammL27NnIyclBcHBwFdaQiIiIqPrR+J7AqKgoAEDv3r3l5rm5uQEAoqOjS1xefHw8UlNTkZ+fDxsbG/Ts2RN16tQp8frZ2dklXpaoMrBNkhiwnZMYlLedGxoalmp5jQ+BDx8+BAA0bdpUbp6FhQWMjY2RkJBQ4vJ+//13mf8bGRlh/vz5mDlzZonWf/78OfLz80v8fkQVLTExsaqrQFTh2M5JDMrTznV0dNCkSZNSraPxITAjIwMAYGJionB+rVq1hGVUsbW1xcqVK+Hm5gYrKyu8efMGkZGR+O6777B06VIYGRnhyy+/LLYcKyur0m2AQm/UUAZRIWtr66qughJs56Q+mtnO2cZJvSq7nWt8CFQXFxcXuLi4CP83MjKCr68vPvroI/Tq1QvLly/H+PHjoaurepeUtquVqKKxTZIYsJ2TGFR2O9f4C0OkPYDKevsyMzOV9hKWROvWrdGlSxe8efMGd+/eLXM5RERERNWJxodA6VhA6djAolJSUpCVlVXqc+Afqlu3LgDg3bt35SqHiIiIqLrQ+BDo7OwMADh9+rTcvFOnTsksUxb5+fmIi4sDoKljToiIiIjUr8QhcMWKFdixY4fCeTdv3sTjx4+Vrjtv3jwMGjSo1JUDAFdXV9jZ2SEkJATx8fHC9PT0dKxevRr6+vrw9fUVpicnJ+PevXtIT0+XKefatWtyZefn52Pp0qVISEhA9+7d0aBBgzLVkYiIiKi6KfGFIcuXL0eXLl0UPm2je/fu6Nq1K8LCwhSue+PGDVy6dKlsFdTVxZo1a+Dl5QUPDw+Zx8YlJiYiMDAQtra2wvIBAQEIDg7G+vXrZeras2dPtGnTBm3atBGuDo6OjsaDBw/QsGFDrF27tkz1IyIiIqqO1HZ1sEQiUVdRcnr06IHw8HAEBQUhNDQUubm5sLe3R0BAADw9PUtUxrRp03DlyhX89ddfePPmDfT19dG4cWPMnTsX06ZNg5mZWYXVn4iIiEjTVJtbxHTo0AEhISHFLrdhwwZs2LBBbvqyZcsqolpERERE1ZLGXxhCREREROrHEEhEREQkQgyBRERERCLEEEhEREQkQgyBRERERCJUqquDL126hDp16shN19LSUjqPiIiIiDRPqUJgee4FqKWlVeZ1iYiIiEi9ShwCDx8+XJH1ICIiIqJKVOIQ6OLiUpH1ICIiIqJKVOILQ1asWIEdO3ZUZF2IiIiIqJKUOAQuX74c27dvr8i6EBEREVEl4S1iiIiIiESIIZCIiIhIhBgCiYiIiESIIZCIiIhIhEp1s+ikpCSsWLGizG82f/78Mq9LREREROpTqhD47NkzhkAiIiKif4BShUADAwPUq1evoupCRERERJWkVCGwbdu2OHbsWEXVhYiIiIgqCS8MISIiIhIhhkAiIiIiEaqUEHjlyhXMnDmzMt6KiIiIiEqgwkLgq1evsHbtWnTt2hV9+/bF1q1bK+qtiIiIiKiUSnxhSKNGjdCgQQOVyxQUFOD48ePYvn07IiIikJeXB4lEAgDo0KFD+WpKRERERGpT4hB448YNpfPu3buHHTt2YPfu3fj7778BABKJBPXq1cOwYcMwatQotGrVqvy1JSIiIiK1KNUtYorKysrC/v37sX37dly5cgVAYfDT09NDbm4uzM3NcefOHejo6KitskRERESkHqUOgdHR0di+fTsOHz6Md+/eCad7HR0dMWLECHh7e6N58+bQ0dFhACQiIiLSUCUOgT/99BN27tyJR48eCcGvXr168PHxwYgRI9CmTZsKqyQRERERqVeJrw5etmwZHj16BH19fQwePBjBwcG4c+cOvv/++0oJgLGxsfDx8YGNjQ2srKzg7u6O0NDQMpeXlpaG1q1bw8zMDF5eXmqsKREREZHmK/XpYF1dXRgaGsLIyKjSTvdGRkbCy8sLhoaG8PT0hLGxMQ4dOgQ/Pz8kJSVh+vTppS7zq6++QkZGRgXUloiIiEjzlbgn8KuvvkKjRo3w9u1b7NmzB0OHDoWDgwOWLVuGhw8fVlgF8/Ly4O/vD21tbRw9ehT/+te/8P333yMqKgrNmjVDYGAgnj59WqoyDx48iL1792Lp0qUVU2kiIiIiDVfiELhw4UJcv34doaGhGDp0KAwMDPDs2TOsXr0anTp1Qt++fbFlyxakp6ertYKRkZF49OgRvL294eTkJEw3NTXF7NmzkZOTg+Dg4BKX9+rVK8yZMwfDhw9H37591VpXIiIiouqiVE8M0dLSQs+ePbFp0ybcuXMHK1euhJOTEyQSCS5fvozZs2ejZcuWAID8/HwUFBSUu4JRUVEAgN69e8vNc3NzA1B4xXJJzZo1Czo6OlixYkW560ZERERUXZX5PoFmZmaYMGECJkyYgFu3bmHbtm0ICQnB69evAQCvX79Gy5YtMWzYMIwcORL29vZleh/pqeamTZvKzbOwsICxsTESEhJKVNbu3btx+PBh7NixA2ZmZmXqtczOzi71OkQViW2SxIDtnMSgvO3c0NCwVMuXOQQW1aZNGyxfvhyBgYEICwvDjh07cPr0abx69QobNmzAhg0b0K5dO5w6darUZUsv3jAxMVE4v1atWiW6wOPFixeYP38+vL294eHhUep6SD1//hz5+fllXp9I3RITE6u6CkQVju2cxKA87VxHRwdNmjQp1TpqCYFSenp6GDx4MAYPHowXL15g586dCA4OxsOHDxEXF6fOtyq1GTNmQE9Pr9ynga2srNRQmzdqKIOokLW1dVVXQQm2c1IfzWznbOOkXpXdztUaAouytLTEnDlzMGfOHERHR2Pnzp1lKkfaA6isty8zMxNmZmYqy9i5cyciIiLwn//8B3Xr1i1TPaRK29VKVNHYJkkM2M5JDCq7nVdYCCzK2dkZzs7OZVpXOhbw4cOHaNu2rcy8lJQUZGVloX379irLiI+PBwB8/vnnCuefOnUKZmZmcHBwEC5EISIiIvonq5QQWB7Ozs5YvXo1Tp8+LfdkD+kYw+ICZufOnfH27Vu56W/fvsX+/fvRsGFD9O7dG40aNVJfxYmIiIg0mMaHQFdXV9jZ2SEkJARffvmlcK/A9PR0rF69Gvr6+vD19RWWT05ORkZGBiwsLGBqagoA8PT0hKenp1zZT548wf79+9GqVSusXbu2cjaIiIiISAOU6j6BVUFXVxdr1qxBQUEBPDw84O/vj2+++QYuLi548OABFi9eDFtbW2H5gIAAdO7cGUeOHKnCWhMRERFpNo3vCQSAHj16IDw8HEFBQQgNDUVubi7s7e0REBCgsIePiIiIiFSrFiEQADp06ICQkJBil5Pel7AkbG1tkZaWVs6aEREREVU/Gn86mIiIiIjUjyGQiIiISIQYAomIiIhEiCGQiIiISIQYAomIiIhEiCGQiIiISIQYAomIiIhEiCGQiIiISIQYAomIiIhEiCGQiIiISIQYAomIiIhEiCGQiIiISIQYAomIiIhEiCGQiIiISIQYAomIiIhEiCGQiIiISIQYAomIiIhEiCGQiIiISIQYAomIiIhEiCGQiIiISIQYAomIiIhEiCGQiIiISIQYAomIiIhEiCGQiIiISIQYAomIiIhEiCGQiIiISIQYAomIiIhEiCGQiIiISIQYAomIiIhEqNqEwNjYWPj4+MDGxgZWVlZwd3dHaGhoidePiIjAuHHj0KlTJ9jY2MDS0hKdOnXCtGnT8ODBgwqsOREREZHm0a3qCpREZGQkvLy8YGhoCE9PTxgbG+PQoUPw8/NDUlISpk+fXmwZJ06cwOXLl9GxY0e4u7tDT08Pd+/eRXBwMPbu3Ys9e/bA1dW1EraGiIiIqOpppaWlSaq6Eqrk5eWhU6dOeP78OSIiIuDk5AQASE9Ph5ubG54+fYorV67AxsZGZTnZ2dkwNDSUm3727FkMHjwY7dq1w5kzZypkGz7U5teYSnkfEodbUzpXdRUUYjsnddLEds42TupW2e1c408HR0ZG4tGjR/D29hYCIACYmppi9uzZyMnJQXBwcLHlKAqAAODq6gozMzMkJCSorc5EREREmk7jTwdHRUUBAHr37i03z83NDQAQHR1d5vJjYmKQlpaGrl27lmj57OzsMr8XUUVgmyQxYDsnMShvO1fW4aWMxofAhw8fAgCaNm0qN8/CwgLGxsal6sU7ffo0Ll26hJycHDx8+BDHjx9H3bp18cMPP5Ro/efPnyM/P7/E70dU0RITE6u6CkQVju2cxKA87VxHRwdNmjQp1ToaHwIzMjIAACYmJgrn16pVS1imJE6fPo1169YJ/2/SpAn+/e9/o23btiVa38rKqsTvpdwbNZRBVMja2rqqq6AE2zmpj2a2c7ZxUq/KbucaHwLVbdmyZVi2bBmysrJw9+5drFy5Ev369cO6devg4+NT7Pql7WolqmhskyQGbOckBpXdzjX+whBpD6Cy3r7MzEylvYSqGBsbo0OHDtixYweaN2+OmTNn4tWrV+WqKxEREVF1ofEhUDoWUDo2sKiUlBRkZWWV+hx4Ubq6uujevTvevn2LuLi4MpdDREREVJ1ofAh0dnYGUDiW70OnTp2SWaaskpOTAQB6enrlKoeIiIioutD4EOjq6go7OzuEhIQgPj5emJ6eno7Vq1dDX18fvr6+wvTk5GTcu3cP6enpMuUo6+U7deoUjhw5AlNTU3Tq1KliNoKIiIhIw2j8hSG6urpYs2YNvLy84OHhIfPYuMTERAQGBsLW1lZYPiAgAMHBwVi/fj1GjhwpTO/Vqxfs7e3Rpk0bWFlZ4d27d7h58yYuXLgAPT09rFu3DjVr1qyKTSQiIiKqdBofAgGgR48eCA8PR1BQEEJDQ5Gbmwt7e3sEBATA09OzRGUsWbIE586dQ3R0NF69egVtbW00atQIY8eOxeTJk9GyZcsK3goiIiIizaHxzw7+J+LzJkmdNPGZqgDbOamXJrZztnFSNz47mIiIiIgqHEMgERERkQgxBBIRERGJEEMgERERkQgxBBIRERGJEEMgERERkQgxBBIRERGJEEMgERERkQgxBBIRERGJEEMgERERkQgxBBIRERGJEEMgERERkQgxBBIRERGJEEMgERERkQgxBBIRERGJEEMgERERkQgxBBIRERGJEEMgERERkQgxBBIRERGJEEMgERERkQgxBBIRERGJEEMgERERkQgxBBIRERGJEEMgERERkQgxBBIRERGJEEMgERERkQgxBBIRERGJEEMgERERkQgxBBIRERGJULUJgbGxsfDx8YGNjQ2srKzg7u6O0NDQEq0rkUgQERGB2bNno1u3brCxsYGlpSWcnZ3x008/ITs7u4JrT0RERKRZdKu6AiURGRkJLy8vGBoawtPTE8bGxjh06BD8/PyQlJSE6dOnq1z//fv38PHxgYGBAVxcXODm5obs7GycPn0agYGBOHr0KI4cOYIaNWpU0hYRERERVS2ND4F5eXnw9/eHtrY2jh49CicnJwDAvHnz4ObmhsDAQAwePBg2NjZKy9DR0cGiRYvwxRdfwMzMTJiem5uL0aNHIzw8HH/++SdmzJhR0ZtDREREpBE0/nRwZGQkHj16BG9vbyEAAoCpqSlmz56NnJwcBAcHqyxDT08Pc+fOlQmA0umzZ88GAERHR6u97kRERESaSuN7AqOiogAAvXv3lpvn5uYGoHwBTk9PD0Bhb2FJcPwgaRq2SRIDtnMSg/K2c0NDw1Itr/Eh8OHDhwCApk2bys2zsLCAsbExEhISylz+9u3bASgOmYo8f/4c+fn5ZX4/InVLTEys6ioQVTi2cxKD8rRzHR0dNGnSpFTraHwIzMjIAACYmJgonF+rVi1hmdKKiIjA5s2b0bJlS4wePbpE61hZWZXpvWS9UUMZRIWsra2rugpKsJ2T+mhmO2cbJ/Wq7Hau8SGwosTGxmLcuHEwMTHBli1bYGBgUKL1StvVSlTR2CZJDNjOSQwqu51r/IUh0h5AZb19mZmZSnsJlYmLi8PQoUOhpaWF/fv3o3Xr1uWuJxEREVF1ovEhUDoWUDo2sKiUlBRkZWWV6hx4XFwchgwZAolEgv3796N9+/ZqqysRERFRdaHxIdDZ2RkAcPr0abl5p06dklmmONIAWFBQgJCQEHTs2FF9FSUiIiKqRjQ+BLq6usLOzg4hISGIj48Xpqenp2P16tXQ19eHr6+vMD05ORn37t1Denq6TDnXrl3DkCFDkJ+fj71796Jz586Vtg1EREREmkbjLwzR1dXFmjVr4OXlBQ8PD5nHxiUmJiIwMBC2trbC8gEBAQgODsb69esxcuRIAMCbN28wZMgQpKenw93dHWfOnMGZM2dk3sfU1BRTpkyp1G0jIiIiqioaHwIBoEePHggPD0dQUBBCQ0ORm5sLe3t7BAQEwNPTs9j1MzIykJaWBgA4efIkTp48KbeMtbU1QyARERGJRrUIgQDQoUMHhISEFLvchg0bsGHDBplptra2QggkIiIiomowJpCIiIiI1I8hkIiIiEiEGAKJiIiIRIghkIiIiEiEGAKJiIiIRIghkIiIiEiEGAKJiIiIRIghkIiIiEiEGAKJiIiIRIghkIiIiEiEGAKJiIiIRIghkIiIiEiEGAKJiIiIRIghkIiIiEiEGAKJiIiIRIghkIiIiEiEGAKJiIiIRIghkIiIiEiEGAKJiIiIRIghkIiIiEiEGAKJiIiIRIghkIiIiEiEGAKJiIiIRIghkIiIiEiEGAKJiIiIRIghkIiIiEiEGAKJiIiIRIghkIiIiEiEqk0IjI2NhY+PD2xsbGBlZQV3d3eEhoaWeP1Hjx4hKCgIvr6+aN26NczMzODo6FiBNSYiIiLSXLpVXYGSiIyMhJeXFwwNDeHp6QljY2McOnQIfn5+SEpKwvTp04st4/z581ixYgV0dHTQsmVLpKSkVELNiYiIiDSTxofAvLw8+Pv7Q1tbG0ePHoWTkxMAYN68eXBzc0NgYCAGDx4MGxsbleU4OzsjIiICDg4OMDIygoWFRWVUn4iIiEgjafzp4MjISDx69Aje3t5CAAQAU1NTzJ49Gzk5OQgODi62HDs7O3Tq1AlGRkYVWV0iIiKiakHjQ2BUVBQAoHfv3nLz3NzcAADR0dGVWiciIiKi6k7jTwc/fPgQANC0aVO5eRYWFjA2NkZCQkKl1Sc7O7vS3ouoJNgmSQzYzkkMytvODQ0NS7W8xofAjIwMAICJiYnC+bVq1RKWqQzPnz9Hfn5+pb0fUXESExOrugpEFY7tnMSgPO1cR0cHTZo0KdU6Gh8CNY2VlZUaSnmjhjKICllbW1d1FZRgOyf10cx2zjZO6lXZ7VzjQ6C0B1BZb19mZibMzMwqrT6l7WolqmhskyQGbOckBpXdzjX+whDpWEDp2MCiUlJSkJWVVeruTyIiIiKx0/gQ6OzsDAA4ffq03LxTp07JLENEREREJaPxIdDV1RV2dnYICQlBfHy8MD09PR2rV6+Gvr4+fH19henJycm4d+8e0tPTq6K6RERERNWCxo8J1NXVxZo1a+Dl5QUPDw+Zx8YlJiYiMDAQtra2wvIBAQEIDg7G+vXrMXLkSGH669evsWjRIuH/ubm5SE1NxeTJk4Vpy5YtQ926dStnw4iIiIiqkMaHQADo0aMHwsPDERQUhNDQUOTm5sLe3h4BAQHw9PQsURlZWVlyTxZ5+/atzLSvv/6aIZCIiIhEoVqEQADo0KEDQkJCil1uw4YN2LBhg9x0W1tbpKWlVUDNiIiIiKofjR8TSERERETqxxBIREREJEIMgUREREQixBBIREREJEIMgUREREQixBBIREREJEIMgUREREQixBBIREREJEIMgUREREQixBBIREREJEIMgUREREQixBBIREREJEIMgUREREQixBBIREREJEIMgUREREQixBBIREREJEIMgUREREQixBBIREREJEIMgUREREQixBBIREREJEIMgUREREQixBBIREREJEIMgUREREQixBBIREREJEIMgUREREQixBBIREREJEIMgUREREQixBBIREREJEIMgUREREQiVG1CYGxsLHx8fGBjYwMrKyu4u7sjNDS0VGW8f/8eK1asQPv27WFhYYFWrVrB398fL1++rKBaExEREWkm3aquQElERkbCy8sLhoaG8PT0hLGxMQ4dOgQ/Pz8kJSVh+vTpxZZRUFCAESNG4NSpU+jUqRMGDRqEhw8fYuvWrTh79ixOnjwJc3PzStgaIiIioqqn8SEwLy8P/v7+0NbWxtGjR+Hk5AQAmDdvHtzc3BAYGIjBgwfDxsZGZTk7d+7EqVOn4O3tjT/++ANaWloAgH//+9+YPXs2li1bhl9++aWiNwcAUNtQ43c7UbmxndM/Hds4VXdaaWlpkqquhCqnT5+Gp6cnRo4cifXr18vM27lzJ6ZMmYIFCxZg/vz5Ksvp27cvYmJiEB8fLxMYJRIJ2rVrh5cvX+LBgwcwMjKqkO0gIiIi0iQaPyYwKioKANC7d2+5eW5ubgCA6OholWVkZ2fjypUraN68uVyPoZaWFnr16oW3b98iLi5OTbUmIiIi0mwaHwIfPnwIAGjatKncPAsLCxgbGyMhIUFlGY8ePUJBQQGaNGmicL50uvS9iIiIiP7pND4EZmRkAABMTEwUzq9Vq5awTHFlmJqaKpwvLbu4coiIiIj+KTQ+BBIRERGR+ml8CCyuly4zM1NpL+GHZaSnpyucX1xvIxEREdE/jcaHQOlYQEXj9VJSUpCVlaV0rJ+UnZ0dtLW1lY4dlE5XNO6QiIiI6J9I40Ogs7MzgMJbxXzo1KlTMssoY2RkhA4dOuD+/ft4+vSpzDyJRIIzZ86gZs2aaNeunZpqTURERKTZND4Eurq6ws7ODiEhIYiPjxemp6enY/Xq1dDX14evr68wPTk5Gffu3ZM79fv5558DAL777jtIJP93a8TNmzfj8ePH8PHx4T0CqwEPDw+YmZlVdTXKZPLkyTAzM8OTJ0+quipEpAbnzp2DmZkZgoKCqroqpCGqW5vQ+BCoq6uLNWvWoKCgAB4eHvD398c333wDFxcXPHjwAIsXL4atra2wfEBAADp37owjR47IlDNixAi4ubkhJCQEffv2xdKlSzFmzBjMmTMHtra2WLRoUWVvWpnEx8dj1qxZ+Pjjj2FtbY169eqhRYsWGDJkCNauXYtXr15VdRU1RmX/MVa3P34qmydPnsDMzEzmZWlpiVatWmHQoEH4/vvv8ejRI7n1pO3DzMwMPXv2VFp+RESEsJyHh4fMvKCgIJiZmWHfvn3q3iz6/6Sfr5eXV1VXpcoVbbPSV/369eHo6IgpU6bwtmr/ANXimTc9evRAeHg4goKCEBoaitzcXNjb2yMgIACenp4lKkNbWxs7d+7Ezz//jN27d+PXX39F7dq1MXr0aCxatEjjnxtcUFCAJUuWYN26ddDR0UG3bt3Qq1cv1KxZEy9fvsTly5exePFiLF++HJcvX4aVlVVVV5noH61x48YYNmwYACAnJwcvX75EbGwsfvzxR6xevRr+/v5YvHix8IhKKV1dXVy7dg03b96Eg4ODXLnbtm2Drq4u8vLyKmU7qOw6dOiAmJgY1K1bt6qrUqHatm2Lfv36ASi8kPLSpUvYuXMnjhw5glOnTqF58+ZVXEPNUd3aRLUIgUDhjg0JCSl2uQ0bNmDDhg0K5xkYGODrr7/G119/re7qVbjAwECsW7cOH330ETZv3qzwYphr165h6dKlyM7OroIaEolLkyZNsGDBArnpFy5cwJdffonVq1dDW1tb7iyDm5sbIiIisH37dixfvlxm3uvXrxEeHo4+ffrg2LFjFVp/Kr8aNWqgRYsWVV2NCteuXTu5tj5r1ixs3rwZP/30E3777bcqqpnmqW5tQuNPBxPw4MEDrFmzBubm5ti3b5/Sq6Hbtm2LAwcOyDwaT3pqY/Lkybh79y5GjhyJxo0by4xNy8vLw7p16+Ds7IwGDRrAxsYGn376qcIvIenpqHPnzsnN27FjB8zMzLBjxw6F75+QkICRI0fC1tYWVlZWGDx4MG7cuKFwWy5cuIABAwbAysoKjRs3hp+fH5KSkkq8z4KCgjBw4EAAwIoVK2ROZ0i3WzpG7/Hjx1i7di0+/vhj1K9fH5MnT5aZr2gM34f7oSTvJyWRSPDbb7+hU6dOqF+/PhwcHLB8+XIUFBSUePtIc3Xt2hX79u2DgYEB1qxZI9durays0KtXL+zduxc5OTky83bv3o2cnByMGjWqMqtMZaRsCIijoyMcHR2RlZWF+fPno1WrVqhfvz66deuGgwcPKiwrJycH69atQ48ePWBlZYVGjRrhk08+QVhYmNyyDx48wJIlS9CjRw80btwYFhYW6NChA5YuXYqsrCy55aVjqbOzs7Fs2TK0bdsW5ubm5Rq6Mnr0aADA9evX5eZlZmbihx9+QJcuXYTvFE9PT1y4cEFhWTdv3oSPjw8aNWoEGxsb+Pj44Pbt2wqPwUW/Z44dO4Z+/fqhUaNGcHR0FJYpzb5MT0/H999/j48//hgNGzaEtbU12rVrh0mTJslcSJqdnY21a9fC2dkZNjY2sLKygqOjI8aOHSvzPaZqWNDt27cxduxYNGvWDPXr14eTkxO+/vprpKamyi1bljZUFtWmJ1DMgoODkZ+fj7Fjx5botLWurvzH+ujRI/Tp0wf29vYYMWIEUlNToa+vD4lEgjFjxiAsLAzNmjXDF198gXfv3mH//v347LPP8P3332Pq1Knl3oanT5/C3d0drVq1wqhRo/Do0SOEhYVh4MCBiImJQf369YVlz549C29vb2hra2Po0KGwtLTE2bNn0b9/f6VPffmQi4sLnj59iuDgYDg7O8PFxUWY92EZ8+bNw+XLl9G3b1/079+/TEMDSvN+S5YsQXR0NPr164fevXvj6NGjWL58OXJzc7F48eJSvzdpnubNm2PIkCHYvXs3jh49ii+//FJm/qhRo3Dq1CkcO3YMgwcPFqZv374drVu3RseOHSu7yqRmeXl58PT0RFpaGgYOHIj//e9/2L9/P8aOHYt9+/ahd+/ewrLv37+Hl5cXoqKi4OjoiFGjRiEvLw8nTpzAiBEjsHLlSkycOFFY/vDhw9i2bRu6d+8OFxcXFBQU4MqVK/jll18QHR2NsLAw6OnpydVpzJgxuHnzJtzc3GBqaioznr6sdHR0ZP7/5s0bDBgwAHfu3EGXLl3g5+eHzMxM4Xi/ZcsWfPrpp8LyN27cwIABA/D27VsMHDgQTZs2RVxcHPr3769wuITUwYMHcfr0afTr1w/jx49HZmYmgNLtS4lEAi8vL1y5cgVdunSBm5sbtLW1kZiYiGPHjsHX11foVJk8eTJCQ0PRpk0bjBgxAgYGBnj27BnOnTuHuLg4mRCqyIULF+Dl5YWcnBwMHjwYNjY2iImJwW+//Ybjx4/j5MmTcqeQS9OGyoohsBqIiYkBAHTv3r3MZVy8eBHz5s3DwoULZaYHBwcjLCwMzs7OCA0Nhb6+PoDCrv6ePXvi22+/hYeHB+zs7Mr83gAQHR2NpUuXYubMmcK0ZcuWYdWqVdixYwdmzZoFoHDso7+/P/Ly8hAWFoauXbsCKPxjnThxIvbu3Vui95Puq+DgYLi4uCg8bSd169YtREZGwtrauoxbV7r3u379OqKjo9GgQQMAhSG0ffv22LhxI+bPny98BlS9ubi4YPfu3YiNjZWb5+HhgTp16mDHjh1CCIyNjcXt27exbNmyyq4qVYAXL16gXbt2OHLkiPA37ePjg8GDB2P9+vUyX+ArV65EVFQUvvrqKyxcuFAYR5qZmYlBgwZh0aJFGDhwICwtLQEAw4cPx9SpU+WOFStWrBDGzkvHq35Yp+joaNSuXbvc27dt2zYAEI7RUvPmzcOdO3ewZs0ajBkzRpj+8uVL9OrVCzNnzoS7uzsMDQ2F5TMzM/HHH3/Ax8dHWP7777/Hjz/+qPT9T548if3798tdZFWafXn79m1cuXIFHh4eMmewgMIwmZubC6Cwt/DAgQNo27YtTp06JRN88/PzhQCqTEFBAaZMmYJ3795h3759cHNzE+YtWbIEa9aswbfffot169bJrFeaNlRWPB1cDfz9998AIBwAijp37hyCgoJkXopO1VpYWGDu3Lly04ODgwEU3jqn6AHF2toaU6ZMQV5eHvbs2VPubbC1tcWMGTNkpklPJxT9krxw4QIeP36Mfv36yRxctLS0sHjxYrlfneowffr0cgXA0vrqq6+EAAgAdevWxYABA5CZmYn79+9XWj2oYkn/XhWd6tHX14ePjw9OnTqFFy9eACjsBdTT05O55RVVbz/88IPMcdXV1RXW1tYyx7yCggJs2rQJjRs3lgktAFCrVi3MmzcPOTk5OHz4sDDdyspK4Y9FaQ/XX3/9pbA+CxYsKFMAjIuLE75fFi5ciN69e2Pz5s1o1qyZzPfK69evsX//fvTo0UMmAAJAvXr1MH36dLx69Uqo39OnT3HhwgU4ODjIBEAAmDlzpsrbgQ0YMEAuAJZlXwJQeHs4AwMDGBsbAyj8/pFIJDA0NIS2tmxs0tHRKfa2ZRcvXhTOxhUNgEBhCK5duzZCQkLkhocAJWtD5cGewGouKioKK1askJv+Ya+hg4ODwoNGfHw8atSogQ4dOigtQ9m4vdJwdHSU++Np2LAhANnH+d28eRMA0K1bN7kybGxs0LBhQ7kbfpeXom2vSG3btpWbpmhf0D/bqFGj8PvvvyM4OBhTpkzBvn370K9fP5ibmyMlJaWqq0flZGpqqvAMSsOGDYWzOwBw//59pKWlwdLSUu5CIaAwWEmXk5JIJNi+fTt27tyJO3fuICMjQ2ZMcXJyssI6lfVYd+3aNVy7dk1mWvPmzREeHi5zCjM2Nhb5+fnIyclROCZO+nSu+/fvo3///sLxvkuXLnLL1qxZE46Ojgo7NZRtS2n3ZcuWLdGmTRuEhITg2bNn8PDwgIuLC5ycnGS+r0xMTNC3b1+cOHECPXr0wJAhQ+Di4oL27dsrPO3+Iek9josOE5IyNjZGu3btcPr0ady/fx9t2rQR5pW0DZUHQ2A1UK9ePdy9excvXryQu+powYIFwqnHffv2Yfz48UrLUCQzM1MIIB+ysLAQlimvWrVqyU2Tjl3Mz88Xpkmf46xsXF79+vXVHgKV7ZuKomhfSHs4i+4Lqt6kPXzKbhXh6OiIjz76CDt27ECjRo2Qnp7OC0L+QZQ9i15HR0cmsL158wYAcOfOHdy5c0dpeW/fvhX+PW/ePPzxxx/CBQ8NGjQQfuSvWLEC79+/V1hG0bHXpeHn54eff/4ZEokEycnJ+PXXX7F27Vp8/vnnOHjwoHD8km7LxYsXcfHixWK3Rfrdoup4r4yi43Zp96Wuri4OHz6MoKAgHD58WLiS39zcHBMmTMDcuXOFbduyZQtWr16NvXv3IjAwEEDhZzxixAgsWbIENWrUUPp+0u1U9l2j7Lu2pG2oPBgCq4GPP/4YUVFROHfuHFxdXctUxof3KpOqVauW0htMS09DFw0t0l9HisKKNMCVh7TRF1cndVK2byp6W+mfLSoqCgDQvn17pcuMHj0ac+fOxdKlS2FpaYk+ffpUVvVIQ0iPr4MGDcLWrVuLXf7ly5f4888/0aZNG0RERMiEj5SUFIVnhqSUHetKSktLC5aWlggMDERKSgr27NmD33//HVOmTJHZlmnTppVobKt0+bIc7xVtS2n3JQDUqVMHP/74I1auXIl79+4hMjISGzduRFBQEPT09DB79mwAhbd+WbRoERYtWoTHjx/j3Llz2Lx5M3777TdkZ2fjl19+KXY7X758qXI7FXUQVDSOCawGPvvsM2hra+M///mP0J2tLk5OTnj37h2uXr0qN0/6JVb0qifp2Ifnz5/LLV/0sX5lJb0a7Pz583Lznj59imfPnpW4rPL2rpV2W9mbR1IPHjzAgQMHYGBgIHMl5Ie8vb1haGiI58+fw9fXt0LGvJJma9myJUxMTBAXFydciKDK48ePIZFI0LNnT7neJ2W3YKkI3333HYyMjLBq1SqhB6t9+/bQ0tLC5cuXS1SG9Hh/6dIluXnv3r0TTheXVGn3ZVFaWlpo2bIlJkyYgNDQUABQeq9OOzs7jB49GkePHoWxsXGx9/R0cnIC8H/fqUW9ffsWcXFxMDIyqpKbbjMEVgPNmjWDv78/Xr58CW9vb2FcxYfKMp7ss88+A1D4uL2ifzRJSUlYv349dHV1Za4yk/Zq7Nq1S6Y7OiYmpsRX7qrStWtX2Nra4vjx4zIHNIlEgsDAwFIFLOkA6NIEx6Kk27pz506Z6QcPHkR0dLTa34/+GS5evAhPT0+8f/8eM2fOVPn0Hukj4LZv3y70ppC46OrqYty4cUhMTMSiRYsUhpfbt28LvUjSi9hiYmJkjsHPnj1DQEBA5VQaQIMGDeDn54fU1FThAQ0WFhYYOnQoLl26hDVr1kAikcitd+XKFbx79w5A4TjvLl264MaNG9i/f7/McmvWrBFO75ZUafflkydPFN4HVjrfwMAAQGFP5e3bt+WWS0tLw/v374XllOnSpQsaN26MiIgIuYt2Vq1ahdTUVHh5eVXJnSF4OriaWLx4MXJycrB+/Xp06tQJ3bp1g4ODA2rUqIGXL1/i1q1buHr1KoyNjYu9X1FRvr6+OHz4sHCbmH79+gn3CXzz5g2WLVsmMzC1U6dO6NKlCyIjI9GnTx9069YNiYmJCAsLQ//+/eWe2Vxa2tra+Ne//gUfHx8MGTJEuE9gZGQkUlJS0KZNG9y6datEZbVo0QKWlpbYv38/DAwMYGVlBS0tLUycOLFE9xscMGAAGjdujJ07d+LZs2dwcnISThdIBwmr8/2oeklISBAGv+fm5uLly5e4evUqbt++DR0dHcydO7dETydydnYu9Xtv2rQJJ0+eVDhvzJgxcrftoNKT3qxYkRYtWgi3tVKHBQsW4Pr16/j9999x4sQJdOvWDfXq1cPz589x+/Zt3Lx5ExEREahXrx4aNGiAQYMG4dChQ+jZsydcXV3x999/4/jx43B1dVX43OqKMnPmTGzZsgXr16/HxIkTYWZmhp9++gn379/HkiVLsGvXLnTu3BmmpqZ49uwZ4uLi8PDhQ9y9e1foxVyxYgU8PDwwYcIEHDp0CE2aNMH169dx+fJldOvWDefPn5e7qFCV0uzLGzduYPTo0ejQoQNatmwJCwsLPH/+HGFhYdDW1hZ+mD1//hw9evSAg4MD2rRpAysrK6SmpiIsLAy5ubmYPn26yjppa2vj119/hZeXl/DdZm1tjZiYGERFRaFx48ZYunRpmT+H8mAIrCa0tbXx/fffY9iwYdi8eTPOnz+PuLg4vH//HrVr10arVq0QGBgIX1/fUl3ooKWlha1bt2LDhg0IDg7Gxo0boa+vDycnJ0ydOhUDBgyQW2fnzp1YuHAhjh8/jtu3b8PBwQG7du3Cixcvyh0CAaBnz544ePAgli1bhoMHD8LQ0BCurq7YsmULJk2aVOJydHR0sG3bNnz77bfYt2+fcMpi2LBhJQplRkZGOHDgABYuXIjIyEhcuXIFHTt2RFhYGMLDw+VCYHnfj6qXR48eCeOvjIyMYGpqiubNm+Orr77CiBEj0Lhx4wp77/PnzyscMgEUXoHIEFh+L168EG6h9SFnZ2e1hkADAwOEhIRg27Zt2LVrFw4fPoz379+jXr16aNWqFcaNGwd7e3th+V9//RU2NjY4dOgQNm7ciEaNGmHq1KmYOXOmWp8mUZz69etj3LhxWLduHdavX49vvvkGtWvXxokTJ/DHH39g//792Lt3LwoKCoSnI3311VcyF0t99NFHCAsLQ0BAAE6ePAktLS106dIF4eHhQs9macbKlWZftmvXDjNnzkRUVBROnDiB9PR01K9fH66urpgxYwY6deoEoLDH8uuvv0ZkZCTOnj2L1NRU1K1bFx999BEmTZoEd3f3YuvVtWtXREREYOXKlTh9+jQyMjLQoEEDTJo0SW6fVCattLQ0+f5aIiIioiqSn5+Ptm3bIjs7m/dPrUAcE0hERERVIi8vT+EFjz///DMSExPh4eFRBbUSD/YEEhERUZVIS0tDixYt0KtXLzRt2hS5ubm4evUqYmNj0aBBA/z1118yT1gi9WIIJCIioiqRk5ODBQsWIDIyEsnJycjOzoaFhQX69OmDr776SuXV9VR+DIFEREREIsQxgUREREQixBBIREREJEIMgUREREQixBBIREREJEIMgUREREQixBBIREREJEIMgUREREQixBBIREREJEL/DzTEu6t6mFTdAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot comparison of results\n",
    "categories = ['Ground truth', 'DML', 'Linear Regression']\n",
    "sns.barplot(x=categories, y=[tau, ate_dml, ate_lr])\n",
    "plt.ylabel('ATE')\n",
    "plt.title('Average Treatment Effect comparison')\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "venv",
   "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": "3.11.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}