causal_ml_blog.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "ad753907-4d7a-479b-88d7-1455496c213f",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Anaconda\\lib\\site-packages\\tqdm\\auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
      "  from .autonotebook import tqdm as notebook_tqdm\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import dowhy\n",
    "from dowhy import CausalModel\n",
    "import dowhy.datasets\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9fecf860-0a4a-493e-87ee-9685b710e420",
   "metadata": {},
   "source": [
    "## Set the value of treatment effect"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "13d6407e-d00a-49d6-b51a-11569f60c840",
   "metadata": {},
   "outputs": [],
   "source": [
    "BETA = 8"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "abeb7c5e-c864-4f4f-8c33-73a8d64ebe3f",
   "metadata": {},
   "source": [
    "## Generate synthetic data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "e7b8b08a-f32f-4430-8e0d-1f7d73fb2855",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>X0</th>\n",
       "      <th>Z0</th>\n",
       "      <th>Z1</th>\n",
       "      <th>W0</th>\n",
       "      <th>W1</th>\n",
       "      <th>v0</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.918315</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.102747</td>\n",
       "      <td>0.259252</td>\n",
       "      <td>1.139336</td>\n",
       "      <td>True</td>\n",
       "      <td>5.644823</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.662431</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.218614</td>\n",
       "      <td>-0.596461</td>\n",
       "      <td>0.807450</td>\n",
       "      <td>True</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
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       "      <td>1.0</td>\n",
       "      <td>0.635194</td>\n",
       "      <td>-0.389247</td>\n",
       "      <td>-0.570277</td>\n",
       "      <td>True</td>\n",
       "      <td>8.587625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.575869</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.491916</td>\n",
       "      <td>1.119905</td>\n",
       "      <td>1.771579</td>\n",
       "      <td>True</td>\n",
       "      <td>8.450914</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.386357</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.143346</td>\n",
       "      <td>0.195709</td>\n",
       "      <td>-0.034058</td>\n",
       "      <td>True</td>\n",
       "      <td>9.623193</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         X0   Z0        Z1        W0        W1    v0         y\n",
       "0 -0.918315  0.0  0.102747  0.259252  1.139336  True  5.644823\n",
       "1 -0.662431  1.0  0.218614 -0.596461  0.807450  True  5.005966\n",
       "2  0.414302  1.0  0.635194 -0.389247 -0.570277  True  8.587625\n",
       "3 -0.575869  0.0  0.491916  1.119905  1.771579  True  8.450914\n",
       "4  0.386357  1.0  0.143346  0.195709 -0.034058  True  9.623193"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = dowhy.datasets.linear_dataset(BETA,\n",
    "                                    num_common_causes=2, # confounders\n",
    "                                    num_samples=5000,\n",
    "                                    num_instruments=2, # instrument variables\n",
    "                                    num_effect_modifiers=1, # features\n",
    "                                    treatment_is_binary=True,\n",
    "                                    stddev_treatment_noise=5,\n",
    "                                    num_treatments=1)\n",
    "\n",
    "df = data['df']\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "01dc03da-05e2-4806-a12b-a4570094a7bb",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 600x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(6,5))\n",
    "\n",
    "sns.boxplot(y='y', x='v0', data=df)\n",
    "\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "13a21c68-cc4d-433a-833d-1bfcabde760c",
   "metadata": {},
   "source": [
    "## Visualize causal pathway"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "eb31fdee-a6f5-4977-9e22-f911f23b5e56",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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2NzeNQLi2tlaz1Yzb7a46t9hut5/q2AghhBDyuoYLgABQURle3iggvlU2ZTbw4Kzf0z0uDPqs0Stve3sb8Xgci4uLxlnDwF47l+HhYUQiEbS3t5/JWCqVChKJhBEIc7nckfdxHIeuri4jEHZ2dtLsICGEEHKKGjIA6vbPBgJ7R8eJHE4tPGh3g5+sMQg8h4sBc2f97iWXyxktZBRFqbrW39+PaDSK7u7uMwtajDGk02mjVJxMJmu2mrHb7cYxdX19fXC73WcyRkIIIaRVNHQABABZY7iRrmB2u4SNogqVMYgcB7vAga9TuFE0hpLKwMBgFzhc8NtwMWBHzylu9KiXUqlktJA5uD6vq6sLkUgEg4ODZz7jpigKVldXjUB4cDPLfm1tbUbfwe7ubmo1QwghhDykhg+AOsYYVosKruyUcSNdQVnd+2cJHAeBA0SeA4/jZwcZY1AZoDAGRQMYGDiOQ9DG41KbHaN+O9yS9Wb8jiPLMq5du4aZmRlks9mqa4FAwGghY1a4yuVyRqk4kUigUqkceZ8oikarmb6+Pvj9/jMeKSGEENL4miYA7peXNdzIVLBWULBWVJCXNSgMVRsSOHDgAOjvYXj9msBxEHmgzS4i5BLQ75Yw4JXqNqNoJk3TjBYy29vbVdfcbjcmJydx8eJFU1u2aJqG9fV1IxBubm7W3Ezi8/mMtYPhcJhazRBCCCEn0JQBcD/GGAoKw1ZJxVZZwWZJRUnRoGh7s3w8x0G8O0Pos/Fod4jocAgI2oX7Or+30TDGsLy8jFgshtXV1aprdrsdly5dwuXLly1x1FupVMLKyooRCIvF4pH38TxvnFvc39+PtrY22kxCCCGEHKHpAyA53traGmKxGG7fvl31fkEQjBYyPp/PpNFVY4xhe3sby8vLRquZ/bud93O5XOjt7TXKxafZD5EQQghpJBQAiWFnZwfT09NYWFg41ELm/PnziEQi6OjoMHGEh8myjGQyaWwmyWQyR97HcRw6OjqMMNjV1VX3I/MIIYSQRkEBkBySz+eNFjIHz/3t7e1FNBpFOBy2ZHk1nU4bpeJkMnmoBY7OZrNVzQ56PJ4zHikhhBBiHgqApKZyuYz5+XnMzs4eWnfX2dmJSCSCoaEhSwZBYO/M5NXVVSMQHtz0sl8wGDQ2k3R3d0MUz+Y4P0IIIcQMFADJsRRFwbVr1zA9PX2ohYzf78fU1BRGR0ct358vn88bm0lWVlZQLpePvE8QBITDYSMQ+v1+y4ZcQggh5EFQACQnpmkabt26hVgshq2traprLpcLly9fxqVLl2CzWeNYvHthjGFjY8NYO7i+vl6z1YzH4zFKxb29vQ3x7yOEEELuhQIguW+MMaysrCAejyOZTFZds9lsuHjxIiYnJ+FyuUwa4f0rl8tIJBJGIMzn80fex3Gc0Wqmr68PHR0dNDtICCGk4VAAJA9lY2MDsVgMS0tLVTNogiBgZGQEkUik4U7rYIxhZ2fHWDu4urpa89xip9NZtZnECn0TCSGEkONQACR1kU6nEY/HsbCwUBWWOI7D4OAgotEoOjs7TRzhg5NlGalUypgdTKfTNe/t6Ogw1g6GQiFqNUMIIcSSKACSusrn85idncX8/PyhFjLhcBjRaBS9vb0NXTbNZrNGI+pkMnno36mTJAm9vb1GIPR6vWc8UkIIIeRoFADJqSiXy7hy5QpmZmYOtZDp6OgwWsg0+gyZqqpYX183Zgc3Nzdr3uv3+41ScTgcplYzhBBCTEMBkJwqVVVx/fp1TE9PHyqd+nw+o4VMs4ShYrForB1cWVlBqVQ68j5BENDd3W0EwmAw2NCzooQQQhoLBUByJhhjuHXrFuLxODY2NqquOZ1Oo4WM3W43aYT1xxjD5uamEQjX1tZqtppxu91Gqbi3t7epPg+EEEKshwIgOVOMMSSTScRiMSQSiaprkiQZLWTcbrdJIzw9lUoFiUTCCIS5XO7I+ziOQ1dXlxEIOzs7aXaQEEJIXVEAJKbZ3NxELBbDrVu3qmbGeJ7HyMgIpqamEAwGTRzh6WGMYXd31ziVJJlM1mw1Y7fb0dfXZ/zXjOGYEELI2aIASEyXTqcxPT2N69evHwpBg4ODiEQiCIVCJo3ubCiKgtXVVWPt4M7OTs1729vbjTDY3d1t+SP4CCGEWA8FQGIZxWIRMzMzmJ+fR6VSqbrW09ODSCSC/v7+liiH5nI5IwwmEolDnw+dJEno6ekxNpM0WtNtQggh5qAASCynUqngypUrmJ2dPXQkW3t7O6ampjA8PNzwLWROStO0Q61mav3Y+nw+Y+1gOByGJElnPFpCCCGNgAIgsSxVVXHjxg3E43Hs7u5WXfN6vZicnMT4+HjTtJA5qVKpVNVq5mCfRR3P8+ju7jYCYVtbW0vMnhJCCDkeBUBieYwxLC0tIR6PY319veqaw+HAxMQEJiYm4HA4TBqheRhj2N7eNk4mWVtbg6ZpR97rcrmqWs204ueLEELIHgqApGEwxrC6uopYLIbl5eWqa6IoYnx8HFNTU/B4PCaN0HyyLCOZTBqBMJvNHnkfx3Ho7Ow0NpN0dXW1TEmdEEIIBUDSoLa2thCPx7G4uHiohczw8DCi0WjTtpA5KcYYMpmMUS5OJpNQFOXIe202G3p7e43NJK0cogkhpBVQACQNLZvNYnp6GteuXTsUbgYGBhCNRtHd3W3S6KxFVdWqVjPb29s17w0Gg0a5uKenh1rNEEJIk6EASJpCsVjE3Nwc5ubmUC6Xq66FQiFEo1EMDAzQJoh98vm8MTuYSCQOfd50oiiip6fHCIR+v58+j4QQ0uAoAJKmIssyrl69iunp6UMtZILBICKRCC5cuEDr3Q5gjGF9fd04mWR9fb1mqxmv12usHezt7YXNZjvj0RJCCHlYFABJU1JVFYuLi4jH44dO1fB4PEYLGeqTd7RyuWyEwZWVlUNhWsfzPLq6uoy1gx0dHTQ7SAghDYACIGlqjDHcuXMHsVgMa2trVdfsdrvRQsbpdJo0QutjjGFnZ8dYO7i6ulrz3GKn01m1mYQ+r4QQYk0UAEnL0FvI3Llzp+r9oihibGwMU1NT8Hq9Jo2ucciyjFQqZQTCdDpd896Ojg4jDIZCISq9E0KIRVAAJC1ne3vbaCGzv2kyx3EYHh5GJBJBe3u7iSNsLAdbzciyfOR9kiSht7fX2ExCYZsQQsxDAZC0rFwuh+npaVy9evVQC5n+/n6jhQytaTs5VVWNc4uXl5extbVV895AIFDVaqbVjvQjhBAzUQAkLa9UKhktZEqlUtW1rq4uRCIRDA4OUhB8AIVCoWozycHPr04QBHR3d6O/vx/9/f0IBAL0+SaEkFPUcgGwrGqoqAwqAxTGwIODwAMCx8EpchDoSadlybKMa9euYWZm5tARaoFAwGghQ02RHwxjDJubm8bawbW1tZqtZtxud9W5xXa7/YxHSwghza2pA2BJ0bBVVrFZUrFVUrBWUJGpqNAAMOj/A/TMJ/EcOhwCupwi2h0COhwCAnaBQmGL0TTNaCFz8LQMt9uNy5cv4+LFi9T/7iGVy2Xj3OKVlRXkcrkj7+M4Dl1dXUYg7OzspNlBQgh5SE0XAEuqhhvpCq7slLFdUqEwQLv7T+Q5DiK394TCAdCfQhgAxgCVMSj7Ph0Cz8HGczjnkXAxaEevW6QnnhbCGMPKygpisRhSqVTVNZvNhomJCVy+fJlandQBYwy7u7vGZpJUKlWz1YzdbjcaUff398Plcp3xaAkhpPE1TQDcLCq4slvGtd0KiooGDnszeiLHgb8b+u4HYwwKAxRtLxTyHId2h4CJoB0jfhscIrWzaCVra2uIx+NYWlqqer8gCBgdHUUkEoHP5zNncE1IURSkUilj7eDBZt77tbe3G4Gwu7ubSvSEEHICDR8Al3MyXt0oIllQoGgMAsfBIXDg6zhTp4fBsqqBAXCKPMYCNryhwwmXREGwlezs7GB6ehoLCwuHWsicP38ekUgEHR0dJo6wOeVyOaNUnEgkUKlUjrxPkiSEw2FjdpBCOSGEHK1hA2BZ1fDSWhFzO2XIGoOD5yHx9z/Td780xlBWGWTG4LcJeKrbhfM+iUrDLSafz2NmZgZXrlw51Peut7cX0WgU4XCYvi9OgaZpRquZlZUVbG5u1txM4vP5jEbU4XCYjv4jhJC7GjIA3snJ+G4yj+2yConnYOe5M3+i1RhDQdHAcRxG/Ta8udtFs4EtqFwuY35+HrOzsygWi1XXOjs7EYlEMDQ0REHwFBWLRSQSCSMQHvw66HieN1rN9PX1oa2tjb4uhJCW1VABcP+sn6IxuETe9B26FZWhpGnw2wS8uduFYZoNbEmKouD69euYnp5GJpOpuub3+zE1NYXR0VFan3bKGGPY2toyNpOsra1Vler3c7lcVa1mHA7HGY+WEELM0zABMC9r+OqdLFYLimmzfrXos4E8x+GxLice7XRYZmzkbGmahlu3biEWix06BcPlcuHy5cu4dOkStZA5I7IsI5FIGIHwYH9HHcdx6OzsrGo1Q+cWE0KaWUMEwHRFxddu57BeUuAWeAi8NcNVUdGgAoi22/HmbheFwBbGGEMikUAsFkMymay6ZrPZcPHiRUxOTlILkzPEGEMmkzFKxclk8tARgDq73Y5wOGycTOJ2u894tIQQcrosHwAzFRVfXspiq6zCbYGS73FKqgZFY5hqd+DpHgqBBNjY2EAsFsPS0lLVZgVBEDAyMoJIJAK/32/iCFuTqqpYXV01AuHBpt/7BYPBqnOLqZRPCGl0lg6ABVnDl25nsV5U4BH5urZ2OU1lVYPMgMc6HXi8y0khkAAA0uk04vE4FhYWqpoccxyHwcFBRKNRdHZ2mjjC1pbP541ScSKRQLlcPvI+URTR09NjBEK/308/44SQhmPZAKhoDF9cyiKRlxti5u+gkqpBZcAzPS5MttPicvK6QqFgtJA52M8uHA4jGo2it7eXQoWJGGNYX183GlGvr6/XbDXj9XqNMBgOh2l9JyGkIVg2AP5ovYgX1wpwCjxEi675O05e0WDjOXxw2IegnUpGpFqlUjFayBQKhaprHR0dRgsZ2oxgvnK5bITBlZUV5PP5I+/jeR6hUMgIhO3t7RTkCSGWZMkAuF5U8IVbGWja3qkbjYoxhpyiod8j4b2D3oYpYZOzpaqq0UImnU5XXfP5fEYLGVEUTRoh2Y8xhp2dHWPt4Orqas1zi51Op3FMXV9fH50bTQixDMsFQEVj+PytDNYKe+v+Gv3Vs6IxlFQNT4fdiFApmNwDYwy3bt1CPB7HxsZG1TWn02m0kLHb7SaNkBxFlmWkUikjEB4M8ft1dHQYjahDoRDN7hJCTGO5ANgMpd+DqBRM7gdjDMlkErFYDIlEouqaJEkYHx/H1NQUtSaxqEwmY2wmSSaTh44K1EmShN7eXiMQer3eMx4pIaSVWSoAbpYUfO5m45d+D9JLwQN3S8GNPqtJzs7m5ibi8Thu3rxZtQmB53mMjIxgamoKwWDQxBGSe1FVFWtra0YgPNgcfL9AIFDVaoZK/oSQ02SpAPhcMo/4VgneJij9HlTRGBSN4WfO+9Dtogd2cn8ymQymp6dx7dq1Q+vNBgcHEYlEEAqFTBodOalCoVC1maRUKh15nyAIxrnF/f39CAQCTfeYSAgxl2UCYFHR8IcLaSgqa6rZPx1jDFlFw1SbAz/eS6U78mCKxSJmZ2cxPz9/qE9dT08PIpEI+vv7KSw0AMYYNjc3sby8jOXl5Xu2mnG73VXnFtM6UELIw7JMAJzeKuG5ZB7uBmr4fL+KigaBBz4yGmjKkEvOjizLuHLlCmZmZg61JGlra0MkEsHw8DBtMmgg5XIZyWTS2EySy+WOvI/jOHR1dRlrBzs7OynwE0LumyUCIGMMf3Ijg62yCk8TByONMeQVDc/QjmBSJ6qq4saNG4jH49jd3a265vV6MTk5ifHxcVpP1mAYY9jd3TXWDqZSqZqtZhwOR9VmEjpfmhByEpYIgHdyMr68lIWd55pm528tOUVDm53HX73gb9qZTnL2GGO4ffs2YrEY1tfXq645HA5MTExgYmICDge98GhEiqIglUoZawd3dnZq3tve3m6Ui0OhEJ1bTAg5kiUC4HcSOUxvl+GTmv+BStEYyhrDB4a8CLsls4dDmgxjDKurq4jFYlheXq66Joqi0ULG4/GYNEJSD7lczigVJxKJQ0cK6iRJQjgcNgKhz+c745ESQqzKEgHwUwtpbJdVuJu4/KvTW8K8JezGFJWBySna2tpCPB7H4uLioRYyw8PDiEQiaGtrM3GEpB40TcP6+roRCA82Ed/P5/MZpeJwOAxJohehhLQq0wNgSdXwyau7AAC70PwBEACysorLbXa8tZdmYcjpy2azRgsZRVGqrg0MDCAajaK7u9uk0ZF6KxaLSCQSRiAsFotH3sfzvNFqpq+vD21tbbSZhJAWYnoATOZlfOFWFg6eg9Dk6/90eUVDm13AXxvxmz0U0kKKxSLm5uYwNzd3qIVMKBRCNBrFwMAAhYAmwhjD1taWsZlkbW0NmqYdea/L5apqNUPrRQlpbqYHQL39S73O/f3a7/8O/vT//pfgBQH/5aVbcLr3jlf693/zA5j/wXcgiCL+60tLsLvcVe8fe/RJ/O//4ffxx//un+HWzCvYTNwBADz+Ux/A3/mP/+9Dj2u/srr3APyL4wE4WmTWk1iHLMu4evUqZmZmDrUaCQaDRgsZ2jzQfCqVSlWrmWw2e+R9HMehs7PTCISdnZ3UUoiQJmN6b4it0l5Jql6zDqNveAIAoKkqbrz2Q0w+9XaoioLF+I8A4O6vX8alJ95S9f7RR59EZmsDP/r6F9DZdw6S3QG5fHSX/oclchxKGsN2SUXYTQ+q5GxJkoTJyUlMTEwYLWT0XaU7Ozv4zne+gx/96EeYmprC+Pg4rRNrIjabDYODgxgcHARjDJlMxmhEnUqljCUCjDGsr69jfX0dr776Kux2O3p7e41ASOdQE9L4TA+AmyUVPOpXcjo3EYXN4USlVMT1l3+AyafejttXplEu5OFr70RmawPXX/kBLj3xFtyej6Nc2GuiO/roE+geuoD//P1FeIJt+Edvn8JWcvmYj/ZgeG6vJ+BOWaWdwMQ0PM9jdHQUIyMjuHPnDmKxGNbW1gAA+XweP/jBD/Dqq68aLWScTqfJIyb1xHEc/H4//H4/Ll++DFVVsbq6aswObm9vG/eWy2XcvHkTN2/eBLA3U6yvHezp6aHZYkIakOkBsKIy1HPJkShJOB95FFdf+i4WXvkBABhv3/mLfxef/Y//Ctfv/v7ay98HAPCCgOHoY7A5nLA5Tv9JTp/tVEzff03I3vfjuXPncO7cOayuriIej+P27dsA9p74X331VUxPT2NsbAxTU1Pwer0mj5icBkEQ0Nvbi97eXgB7LwL0tYOJRKJq3ejOzg52dnYwPT0NURTR09NjBEK/30/rSAlpAKYHwNMIQaNveAJXX/oubs68CqVSwfWX9wLfY+96P777uT/EYvxlKLKM63cD4MD4pLFW8KxwAFSNEiCxlu7ubnR3d2NnZwexWAyLi4vQNA2KomBubg7z8/NGC5n29nazh0tOkdvtxtjYGMbGxoySsB4INzY2jNZCiqIYZWRg7wQavVQcDodhs9nM/GcQQmowPQBqjNWxALxn7NEnAQByuYSbM6/g+qsvItDVg67+QYw++iS++7k/wNLsa1h49SUAr68bPGsq5T9iUcFgEG9961vx2GOPYWZmBlevXoUsy2CM4caNG7hx4wb6+/uNFjI049PcOI5DKBRCKBTCG97wBpRKJSQSCSMQFgoF495sNosrV67gypUr4HkeoVDICITt7e30vUKIRZgeAHmOA0N9k9Bw5FEIoghVUfDcn34SuZ0tPP5THwCwFw6/+7k/wLOf/gTy6b2F76N3A+NZa5GuN6SBeTwePPHEE3jkkUeMFjKl0t7mKH3Wp6urC5FIBIODg/Tk3iIcDgeGh4cxPDwMxhh2dnaMtYOrq6vGucWapiGVSiGVSuFHP/oRnE4n+vr6jP9oXSkh5jE9AIqn8Hxhd7lx7lIEN6dfwUtf/RyA12f5xh7bC3svfeWzxv0jb3hT/QdxDIa93cAAsLa2hhs3bkCSJDidTrhcLrhcLuPXkiTREysxlcPhwBve8AZEIhFcu3YN09PTRguR9fV1fPOb30QgEMDU1BRGRkZoU0AL4TgObW1taGtrQyQSgSzLSKVSRiBMp9PGvcViEQsLC1hYWAAAo9VMX18fQqEQtZoh5AyZ3gfw0zfS2CyqcEv1/cH/k49/DN/4xH8xfv+v/+y76B+7DAD4h2+7jO1UAgDQc34Uv/GVvVLwzloSv/k3/goAYCu5DFVR4HB54OvoBAD81jdercvY9OPg3trrxkTQjj/+4z9GPp+veb8gCEYgPBgQD/5eFE3P9KQFaJqGxcVFxOPxqt2iwN7ascuXL+PixYu0/osYrWZWVlaQTCYhy/KR99lsNoTDYWMzCW02IuR0mZ4WOhwC1ovK8Tfep9E3PGEEQJfPj96RS8a1sUffjB98+U+N+3SqomD9zq2qv6dUyKF0p7pZ7sPSsPeqOWATwHEcuru7sbi4WPN+VVWRzWZrNm3dz2azVQXDWoHR6XTSq23ywHiex8jICC5cuICVlRXEYjGkUikAe7tHX3rpJbz22mu4dOkSJicnqdTXwnw+n9FKSFVVrK2tGYFwa2vLuK9SqWBpaQlLS0sAgEAgYKwd7OnpoRe3hNSZ6TOA8c0Snk/V7ySQRlBWNTAAvzgWgFPkwRjD7u4uCoUCCoUCisXikW/1tVf14nA4jgyGB0vQdru9Zb425MGtra0hHo8bT+A6QRAwOjqKSCQCn89nzuCIJRUKhapWM7Ue4wRBQE9PjxEIA4EAPSYR8pBMD4ArORl/tpSFU+AgtMgPdF7RELDx+PBo4L7+nKZpKBaLNQPi/reVSqVu4+V53giJx5Wgab0i2d3dRTwex8LCQtW5sxzHYWhoCNFoFB0dHSaOkFgRYwybm5vG5qL19XXUenpyu91Gqbi3txd2u/2MR0tI4zM9ABYVDZ+8tgsOgL1FzsXNyiouBe14e5/n1D6Goig1A6I+m6j/Wt+xVw+iKFbNJt6rBE0lneaWz+cxMzODK1euHFr31dvbi2g0inA4TC8YyJHK5XLVucUHz63WcRyHrq4uIxB2dnbS9xQhJ2B6AASAP7q+i92KBrfY/AFQ3wDyTI8bkQ4HNjc3sb6+js7OTnR0dJz5AxdjDLIsHzujqL+t57eLzWarKjUfFRhdLhccDgetV2xg5XIZ8/PzmJ2dRbFYrLrW2dmJSCSCoaEhetImNenLZPRycSqVqvnC1eFwoLe31wiELpfrjEdLSGOwRAD8y5Uc5nbK8EnN3zpC0RjKGsP7Br3odnD4wz/8Q6Nc63Q6qx64rLZwnjGGUql0oqBYz/WKHMfB4XCcqARN6xWtS1EUXL9+HdPT08hkMlXX/H4/pqamMDo6Si1kyLEURUEqlTIC4e7ubs1729vbjbWDoVCIvr8IucsSAfBWpoKv3MnBwXMQm7w7cl7W4LPz+NAFP3gO+JM/+ZOau3s7OjqMB66urq6GeuBSVdUoMx9Vgt6/lrFWW4gHoa9XPEnbHFEUKSyaQNM03Lp1C/F4HJubm1XXXC4XLl++jEuXLlELGXJi2WzWCIPJZLLmGmhJkhAOh43HVdqURFqZJQKgyhg+tZDGblmDp879AK1EL/8+2e3CGzr3ZvfK5TJu3rxp7IKrFYb0B67+/n709/c3VY8sWZZPvLml3usVT1KCdjqdDRW+GwVjDIlEAvF4HIlEouqazWbDxYsXMTk5SSU8cl80TcPa2hpWVlawsrKCjY2Nmvf6/X6jEXU4HIYkSWc4UkLMZYkACACvbRbxvVShqdvBlFQNHAd8eCRwZNDd/8C1vLx8aHZkP/2BS++R1QoPXIwxVCqVEwXFeq9XtNvtJypBO53Opv3+PU0bGxuIxWJYWlqq+roJgoCRkRFEIhH4/X4TR0gaVbFYNMLgysrKoXWoOkEQEAqFjCU4bW1t9LNMmpplAmBe1vBHC7vQGOBowt3A+uzfeNCOd5xw9+/9PHB1d3cbgTAYDLb8A5e+XvEkJehyuVy3j6uvVzxJCdpms7X81+mgdDpttJDZP9vLcRwGBwcRjUbR2dlp4ghJI2OMYWtry9hZvLa2VtWqaD+3222sye7t7YXD4Tjj0RJyuiwTAIHXN4N4m3AWUNYYKnc3f/R57n+27n4fuPQwSD2yjqeq6j1L0PsDY73XK560BN0KM7z7FQoFo4XMwfVc4XAYkUgEfX19Tfc4Qc5WpVKpajVTaz02x3HGucX9/f3o7OykzgSk4VkqAK4WFHzhVgYcmmsWkDGGrKIh7Jbws0Peujxp3c8DV1dXV9UDFz1pPjh9veK9ZhT1t7UC+oOQJOnEJehmWq9YqVRw5coVzMzMoFAoVF1rb29HNBrF0NAQPRmTh8YYM84t1lvNKMrRx5Ta7Xb09vYaj6tut/uMR0vIw7NUAASAF1J5vLpZgkvkm+ZkkKKigeeBDwz50OWsf/NjxhjS6XRVj6x7PXDpi577+vrogeuU6OsVT1KCLpVKdV+veJIStMPhaJgXA6qqGi1k0ul01TWfz4fJyUmMjY1Rc3FSN6qqYnV11XiRvb29XfPetrY2Iwx2d3c31YuwZlFWNWyVVORkDSrba8nGAAgcIPAc7DyHNocAn9R8FchaLBcAKyrDZ2+msVVSm2JDiKIxFFUNT4RceLTrbPr6qaqKVCplPHDt7OzUvLetrc1Y9EwPXObQNO2e/RX3B8Z6r1c8eDpLrRK0VdYrMsawtLSEWCx2aHen0+k0WsjQsgdSb/l8vurc4lo/i6Iooqenx+jY4PP5LPGz00oUjWG9qGCzpGKrpGC1oCJTUaGwva4jB78aDAAHQOQ5OAQOXU4RXU4R7Q4BXU6xabuTWC4AAkAiL+NLS9mGLwXrpd8el4ifGfJBMKnHYS6Xq+qRda8HLr3VTF9fH+26tCB9veJJStC1ZoEfhCAIVYHwXoHxLGbhGGNIJpOIx+NYWVmpuiZJEsbHxzE1NUUz3ORUaJqGjY0N40X2xsZGzVl8r9drzA6Gw2Hqb3mK0hUV13YrmN8pIy9r0O5+TXiOg8gBIseB53AokDO2Nxuo3J0ZVPd9LSWew6BXwqWgA30eEXwThXlLBkCgOUrBBUWDcIql3wdxPw9cPp+v6oGr1TYiNDLGGBRFOVEJulgs1n294nEzinoJuh4zzpubm4jH47h582bV9zLP8xgZGcHU1BSCweBDfxxCaimVSkgkEsbj6sH1qjqe5xEKhYzH1fb2dpodfEgaY1jOyZjfKeN2VoasMfDcXklXOCLs3e/fXdGY8XcG7Twm2hwY9dvgbIKjay0bACsqw+dvZbBeVOAR+YZL3WVVg8wYngi93vTZiu73gUsva1CPrObBGEO5XD5RCbre6xX1I/6OC4wnWa+YyWQwPT2Na9euHWoYfu7cOUSjUYRCobqNnZCjMMawvb1tVF3W1tZqNrB3Op1Va7Ktdvyn1d3JyfheKo+d8t5sn43nYOO5U3luUjSGksbAGIND5DHVZsePdTohNfDpZZYNgMDedO6Xl7LYLqsNFQIrKkNZ0zDV7sAzPa6GCUoHH7hWV1drzgy5XK6qc4upR1Zr0NcrnqQEXes4rgehr1c8SQlaURTMzc1hfn7+0HKHnp4eRCIR9Pf3N8zPJWlssiwjmUwa/VwPbmLScRxnHP/Z19eHUChEu9trKKsaXlwrYm6nDFVjcAr8mR0jqzGGkrpXJu5yinhL2I1ulzUqfPfL0gEQALZLKr58O4tMRYW7AUKgHv7GA3a8vc9t+fHey/4HruXlZWQymSPv0x+49DDY1dVFD1wEqqqeqARdKBTqesSfIAhwuVyw2+0oFovY3t6GqqrgOA48z4PnebS1teGRRx7B6Ogofa+SM6W3mllZWUEymazZW9Rms1WtyW6m4z8fxp2sjOdTeeyU1VOd8TuOqjEUVA0Sz+GRDkdDzgZaPgACwFZJwVdv57BTsfZMYFnVUNEYLgbteGvYbdqmj9Oit5o5yQPX/tlBj+dkJ5+Q1sQYO7K/4lGB8UHXK+q7rA8GTVEUEQwG0dXVBbfbXbME7XQ6KSiSulNVFWtra0Yg3NraqnlvIBCoOv6z1VoeyRrD91cLxqyfFSaE2N3ZQOXubOBbe92WWe9/Eg0RAAFgt6ziq3ey2CypcPA8bIJ1whVjDAV179M42WbHUz0u078xT9v9PHAFg8GqBy5qNUMelL5e8SQl6FKpdOjP670ZD+6Q3t8Sp9Zswv4j/u5Vgrbb7VReJg+kUChUtZo56nsY2Jvl1lvN9PX1IRAINPX3XFnV8I3lPG5nK6bO+tWiagx5VYNb5PGT/Z4HOu3LDA0TAIG984K/lyrgRqYCBsAlcKYHLflunz+XyONNIScmgq354K/3yNL/q9VqRhAEhMNhIxD6/f6W/HyR06dp2pHBsFAoYGNjA8lkEplMZq8FxL6HQT3oPeiMH8/zRji81y5o/Yg/+v4nR2GMYXNz0ziZZH19veYGLI/HU9WxoZn6YBYVDV+7k8NKXobrDNf63S/GGHKKBofA4x39bgx6rd/up6ECILD3SV5IV/D91QIysmbabKA+68cYcM4r4ekeFwJ2mtkC9j43+1vNHPfApb+K7e3tpR5Z5ExtbW0hHo/jxo0bUFUVmqYZJeb29na0t7cDQFWQrOd6RVEUT7QL2uVy0cx5iyuXy0gkEsaL7Fwud+R9+vGfeseGjo6Ohn2RUVY1/PntHBIWD386xhjyigabwOFdA14MWHwmsOECoG7/bKDGGBwCfyYLMBljKGt7vYFafdbvpPQHLj0Q5vP5I+/jOM5oNdPX19fQD1yksWSzWaOFzMHy8MDAAKLRKLq7u431iicpQReLxbq2zLHZbDVnEg/2V6T1is2NMYbd3d2q4z9rvTBxOBxVa7JdLtcZj/bByBrDV+9kcTvbGOFPp88EOkUe7znnQY/LuiGwYQMgsPeJvpGp4OX1ErbLKjTGIN0906/ewUG92wNI/xhDPhve2OWkWb/7pD9w6WWN1dXVe/bI2v/ART2yyGkrlUqYnZ3F3NzcoWUMoVAI0WgUAwMDJ3p8YYwdecTfwZCo91esF47jjDK2/rZWYKT1is1BUZSq4z93d3dr3tve3m6Ui0OhkGVnlp9N5DC7XT7TFi/1oodAr8Tjg8N+yx4l19ABUMcYw0pewZWdMm5lK6ioe127bXc7gT/IOkHGGDTsNX+saAwcOHhtPC4F7RgL2OCzWfOHptHczwOX3iNLf+CiWQ5yWmRZxtWrVzEzM3Oo1BYMBhGJRDA8PFy3J09VVY3+ike1ydn/+1q77x+Evl7xJDOLdBJQ48hms1XHf9bqySlJUtWabJ/PV7cxKIoCjuMe6GdkKVPBV+/kIHCAvUGPg9XuhsAxvx3v7Hdb8oVWUwTA/TIVFdf1swAVDYr2+j9P5DiIPAcOewc/GydCs73DoFXGoDAcOj+w2yVios2BQa/UcK9EGs3+B65EIlHzyU6SJPT29hoPXNQji5wGTdOwuLiIWCyGnZ2dqmtutxtTU1MYHx8/03Ckt8w5SQm6nusVJUmqWqd4r7dWnVVqRZqmYW1tzVg7uLGxUfNev99vNKJ+mOM/t7a28MUvfhE2mw3veMc77usEnpKi4TOLGezebftmxeB0UhWVQWYM7+hzYzRgvY05TRcAdYwxpCsatkoqNksKNooq1ksKyiqDngn1f7geCAUO8Np4hJwiOpwi2u0C2h1CU5z514j2P3AtLy9jc3Oz5r1+v98oFYfD4ZbrkUVOF2MMd+7cQTwex+rqatU1u92OiYkJTExMWGqZAmPMaHtzVGA82F+xnk8Fdrv9RCXoe7XdIaejWCxWdWwoFotH3icIQtXxn8Fg8MRfq+effx5zc3PQNA1OpxNvf/vbce7cuRP92WcTOcxsly3d8/d+5BQNbpHDz1mwFNy0AfAojDFkZA3lu8e4qBrAcYBwd6bPLfEU9ixMf+DSZwfv9cDV3d1tPHA1e48scrZWV1cRj8dx+/btqveLooixsTFMTU013Iy0vl7xJCXoWi2eHsT+9YrHzSzabDb6Oa4zxhi2traMJThra2s1G6273e6qc4trtZphjOFTn/oUNjY2kM/n4fV64XA48Mwzz2BsbOye42mG0u9BVi4Ft1QAJM1D75G1/8D1Wt/K+gNXf38/ent7m6pHFjHPzs6O0UJm/5Mmx3EYHh5GJBIx2sg0E1VV7zmjuP/twR3VD0MQhBOXoGm94oOpVCpIJpNGIMxms0fex3EcOjs7q47/1IPNzs4OPvOZzyCdThtLeDweDxwOBx577DFEo9EjQ5CqMXzqRho75cYv/R6kl4Lfc85jqf6AFABJU6hUKlU9su71wNXV1WUEws7OzqZ6oCFnL5fLYWZmBlevXj20ZrW/vx+RSAQ9PT0t+X22v2XOvUrQhULhgY74q0WSpEO9FQ++1UvUtF7xaIwx49xivdVMrUBvt9uNjg3ZbBavvPIKtra24HK5oGkaSqWS8Tm/fPkynnjiiUM/D4vpCr62nIOT55ruGFUAyMoqzvtseM8561QHKACSpsMYQzqdrjpwvdZieLvdXlXWcLvdZzxa0izK5TLm5uYwOzt7qK1LV1cXIpEIBgcHWzIIHmf/esVaM4r7j/g7jfWKJ+mv2MpfO1VVsbq6agTCg5uidOl0GoqiQFEU+P1+2O1242vncDjg8XgwPDyMH//xH68K319eymIpW4FXas5AXlY1MAC/cMGPoEXax1EAJE1PURTjgWtlZaXmAxfweo+svr4+dHd30+wAuW+KouDatWuYnp4+NBMdCAQwNTWFkZER+t56QPqM0nEl6GKxWPf1isfNKOrXW2G9Yj6fNx5TE4kEyuWysaZQVVUwxiAIAjiOM448LJfLsNvt8Pl86O3txTvf+U5IkoStkoI/XcyAR/Os/TtI7w34WJcTbwpZoxk3BUDScnK5XNVmknv1yNp/4Lrf7z/jkZJGpmkabt68iXg8jq2traprbrcbly9fxsWLF+n4w1OkquqxJWj9bb3XK56kBO10OpuiY4GmadjY2MCLL76I2dlZKIoCnucPvcjRj1oURRFerxfhcBjvfve78Vqa4eWNIrxNtvbvoIKiwSly+OujgTM5uew4FABJS9MfuPSyxubmZs3yks/nqzpwnRaak5NgjGFlZQWxWAypVKrqms1mw6VLlzA5OWmpFjKthjEGRVFOVIIuFountl6x1oyi/murN7//vd/7PWxsbEBRFEiSBEmSjDO2gb3PM2PM+L0gCPAEgnD9+M9DZjxcTd6FQ2UMBUXDO/o8GA+avxmRAiAh+5RKpaoeWYVC4cj7eJ5Hd3e3EQjb2tqa+pUrqY/19XXEYjHcvn276oWGIAgYHR3F1NQUzTRbHGMM5XL5RCXoWq2qHtT+ljn3CoxmrVf8+Mc/bqzRFEURHMfBZrMZAVtRlKrve47jwHf2w/HoOxFwOyG0wGNoVlYx6rfhJwfM3wxCAZCQGhhj2N7eNta5rK6u1nzl73K5qlrNOByOMx4taSS7u7uIx+NYWFg41EJmaGgI0WgUHR0dJo6Q1IOmaTVPaTn4ttZSlAfBcdyJS9D6+ryHtbOzg//+3//7oZAH7L1gttvtsNlsEEURmqZBURTIsgxhaBLS2GPw21ujolJQNLglHn9j1G/6pAEFQEJOSJblqh5ZmUzmyPv0Hln6ZpKuri7Ll26IOfL5PGZmZnDlypVDLWR6e3sRjUYRDodNf6Igp09RlHsGxP0zjvU84k8UxUOns9QqQd9rvWIsFsNXv/pV4/uY4zgwxozzgDmOg8/nw9DQEEKhENrb29HR0YHv7QA30s27+/egisqggeGvjwZMPxmEAiAhDyidThubSVKpVM1zi202m9Ejq6+vDx6P54xHSqyuXC5jfn4es7Ozh8qGnZ2diEQiGBoaoiBIwBir6q94XGCs51O8zWarObOYSqXwwgsvQFVVY+cvz/PGjKDT6YTb7Ybb7caTTz6JoaEhAMD/vJ5GXtaafv2fTmMMBZXhr1igKTQFQELqQO+Rpa8dPLjrc79gMGiUi3t6eqgdCDEoioLr169jenr60Ayz3+/H1NQURkdH6XuGnIh+xN9JStAHe1fer3K5jEwmYwRAURTB83u7evWSr95KR28c/dhTb8GX1hgEcLAJrfPiJiureHO3Cz/Wae7GLwqAhJyCfD5ftZmkVj8yURTR09NjBEK/3/x1IcR8jDGjhczm5mbVNafTicnJSVy6dIlayJC6UVXVOA/6uMB4VLWjWCwil8tBVVXwPH9o2Yu++1cvC/M8D6GrH87H3wWvTQJ/Bo97v/dP/jf84Mt/ipEfeyP+2R993Xj/r//sj+P2fBw//gu/hF/89f/n1MextxHEjp8cMLcaRAGQkFPGGDNazaysrGB9fb1mWcbr9RprB3t7e+kJvsUxxpBIJBCPx5FIJKquSZKES5cu4fLly3SCDTlTsiwf2txy5coV3Lp1C8Vi0WiRVav9C7C3MYTvG4V98hm0u89m09zCay/hNz70LgDAv/vqj9A9dAGbiTv4xz8RAQD82h9/AxceefzUx5FXNLQ7BPzVC+bu+KcASMgZK5fLSCQSRiDM5/NH3sfzPLq6uoy1gx0dHTQ72MI2NjYQj8dx69atQy1kRkZGEIlEqIUMMc3zzz+P2dlZ7O7uIhAIQBAEyLKMUqlkVEBEUawqDXsvPopC+OKZbgD5F+9/CsvX5vDuv/Ur+Ll/+Ov4+if+Cz798Y8hdG4Yv/n1l89kDAVFg9/G48OjgTP5eLVQACTERIwx7OzsGJtJVldXa+7wczqdVZtJqHFwa0qn05iensb169ervlc4jsPg4CAikQi6urpMHCFpRV//+tdx48YNZDIZOBwOVCoVaJoGQRDgcDhgt9vB8zxCoRDGx8dx/vx5xHcVvLhagOcMA+Czf/L/4n/+q3+IQGc3/sOzs/iNv/5TWIz9CB/4+7+G9/6df3QmY9BbwfziWOBMPl4tFAAJsRBZlpFKpYxAmE6na97b0dFhhMFQKEStZlpMoVDA7Ows5ufnD/WQC4fDiEQi6Ovro1ljcia+8IUvYHl5GblczmgArbeOcTqdGBkZwfj4OAKBgPFnfrRexEtrZxsAS/kcfvUtl1DKZ/GL/+r/wf/89f8DAPDxb8bQ0TtwJmMoKhocIoePjgfP5OPVQgGQEAvLZrNVB67XajUjSRJ6e3uNzSRer/ld5snZqFQquHLlCmZmZg6dXNPe3o5oNIqhoSF6gUBO1ac+9SlsbW1B0zRjtq+vrw/j4+MYGBg4cuf6qxtFfP+MZwAB4A/+9T/Ctz/1+7C73CgX8hh//Cn8009++cw+fkHR4JF4/A2aASSEnISmaVhbWzMC4cHdofsFAoGqVjPNcOA8uTdVVbGwsIB4PH5o5tjr9WJqagpjY2P0vUBOxQsvvIC5uTl4vV6MjY1hbGzs2M1J8a0Snk/mz7wJ9Mr1eXzsfW82fv+//MZ/xVMf+NCZffyCoiFgF/ChEdoEQgh5AMVi0SgVr6ys1OzjJQgCuru70d/fj/7+fgQCASoLNjHGGJaWlhCLxbCxsVF1zel0YmJiAhMTE7DbzT+MnjQPvefg/ZxDfGWnjG+t5OAR+TN/TPp3f/3duP7KD2BzuvDb370Gh/vsWrLkZA09bhE/e953Zh/zKBQACWkCjDFsbm4agXBtba1mqxm32111bjEFgebEGEMqlUIsFsPKykrVNUmSMD4+jqmpKWohQ0yzWlDwuZsZ2HkOIn+2AfCTv/6r+M6n/weefO8v4G//1v/vTD92VlYR7XDg6R5zf/YoABLShCqVChKJhBEIc7nckfdxHIeuri4jEHZ2dtLsYBPa3NxEPB7HzZs3q14Y8DyPCxcuIBKJIBg0d0E6aT2yxvCJqzvQGOAQzmaN6nf+9H8g/txfYOb5b0LTNPz6576DgfHJM/nYwN4Ls5yi4e19HlwKmvvimwIgIU2OMYZ0Om2UipPJZM1WM3a73WhE3d/fD5fLdcajJacpk8lgenoa165dO/Q9cO7cOUSjUYRCIZNGR1rRZxbTWCuo8EhnEwD////X38ULf/YpBLvD+MDf+2d4+mc+fCYfV6doDBWN4WfP+xBymbselwIgIS1GURSsrq4agXBnZ6fmve3t7UYg7O7upjNom0SxWDRayBw8prC7uxvRaBT9/f00G0xO3fOpPOKbpTPfCGKWkqqB54CPjgchnXHZ+yAKgIS0uFwuZ5SKE4nEoZ5yOkmSEA6HjdlBn8/cBczk4cmybLSQOXgiTVtbGyKRCIaHh6mFDDk1c9slfDuRN2UjiBlysoZul4gPDpv/+EkBkBBi0DQN6+vrRiDc3NysuZnE5/MZjajD4bBx/idpPKqq4saNG4jH49jd3a265vF4jBYy9DUm9bZRVPCZmxmIHAebyTNip01f//djnU68udv85TUUAAkhNZVKJaysrBiBsFgsHnkfz/NGq5m+vj60tbW1xKv5ZsMYw+3btxGPx7G2tlZ1zeFwGC1kHA6HSSMkzYYxhs/fyiKZl5u+DFxRGRQwfPC8D11O8/txUgAkhJwIYwzb29tYXl42Ws1omnbkvS6Xq6rVDAWGxsIYw+rqKmKxGJaXl6uuiaKI8fFxTE5O0okzpC6u7pTxzZUcXCIPoYlfOGZlFX1uCe8f8lriBTIFQELIA5FlGclk0thMkslkjryP4zh0dnZWtZqhNWWNY3t7G/F4HDdu3KhaDsBxnNFCpq2tzcQREiu5c+cOXn31VaiqCkEQwPM8BEEw/jv4ewDYTmdwu3sKnOSAS2zOxwaVMRQUDT/Z78FowBq9VykAEkLqIp1OG6XiZDIJRVGOvM9utyMcDhsnk1Aj4saQzWaNFjIHv7YDAwOIRCLo7u62xMwGMc8Xv/hFJJNJVCoVcBxnfD8cfAsA5XIZpVIJjDFww1E4xx9DwC415fdQXtHgFjl8eDRg+u5fHQVAQkjdqaqK1dVVIxBub2/XvDcYDFadW0ytZqytVCphbm4Os7Ozh1rIhEIhRKNRDAwMNOWTODne97//fczMzGBra+vI64wxaJp2qCG54A3C+9afhyQIsJ9RU+izom/+eLzLiTeGzN/8oaMASAg5dfl83thMsrKycig46ERRRE9PjxEI/X4/BQmLkmUZV69exczMzKGTZoLBIKampnDhwgUK9C0mm83i05/+NDKZDMrlMoLBIDiOg6IoKBQKRpspPQhyHAeXy4Unn3wS+f5JzO+U4RF58E30c59TNLhEDj837LPURpeWCICKxrBdVrFVUrFdVvd24mgMMmPgAUg8B4Hj4JZ4dDgEtDsEeKXW6ElEyFljjGFjY8NYO7i+vl6z1YzX6zXCYDgchs1mO+PRkuNomobFxUXEYrFDTcXdbjcmJydx8eJFaiHTQp599llcu3YN29vbsNvt0DQNsiwb1xljxhrBtrY2jI6O4m1vexuKKsNnFjPIVlR4LBSUHoasMZQ1hrf3unHR5KPfDmrKAChrDLcyFawXVawVFWyVVCgag3r3n6r/g/V4x/b9GgBEnoNT4NDlEtHpENHvkRByChQICTkF5XIZiUTCCIQHGxLreJ5HKBQyAmF7ezv9TFoIYwx37txBPB7H6upq1TW73Y5Lly7h8uXLcDqdJo2QnJWtrS186lOfQjqdNoLe/p9VjuPAGEMwGERXVxfe+973Gi8QFtMVfH05B5FDw5eCGWPIKhou+Gz4qQGP5R6vmioA7pRVXNst48pOGTl5rz0FBw4iD4gcB4HDsV8AjTEoGqCw1wOjwHEIuURMBO0477PBJljri0hIs2CMYWdnx1g7uLq6WvPcYqfTaRxT19fXR8HCQtbW1hCLxXD79u2q9wuCgPHxcUxNTVELmSa0v6H47du3UalUoKoqeJ6HKIpwOp3QNA3FYhE+nw9erxcf+MAHqk4VYozhW4k8rjRBKdgo/Z73wWuz3oxmwwdAjTHczsqY3ynjTk6GojEIHAe7wNWlnxBjDArbO78PADwSj/GAHeNBO4J2631BCWkmsiwjlUoZs4PpdLrmvR0dHUYj6lAoRK1mLGBnZ8doIbO/ZyTHcTh//jyi0Sja29tNHCGph6OOFJRlGel0GpqmGaVeWZaRyWTgcrngdrvxrne9C/39/Yf+voKi4TOLGWQqasMeEVdRGSrMmqVfXUMHwK2SgueSBSQLChhjsPF7R8mc1jeLyhjK6t7MoMRziLY78IYup2W2dBPS7LLZrNGIOplMVq0r2k+SJPT29hqBkGabzJXL5TAzM4OrV68e+pr19fUhGo2ip6enIZ/oW1mxWMTs7Czm5+eP3BGez+exs7ODdDoNt9uNQqEASZLg8/nw2GOP4ZFHHqn5dy9lK/j6nRxUDXCJp/e8fhpkjaGkahgP2PETfW7Ljr0hA6DKGOKbJby8UURJZXAJPMQzDGGMMZRUBoUxdDpF/HjYjW6X+ce6ENJKVFXF+vq6MTu4ublZ895AIFDVakYU6efVDOVy2WghUyqVqq51dXUhEolgcHDQsk+YZE8mkzF6Qh5conHu3DlEo1GEQiHcvn0b3/jGN7C7uwtFUSAIAgKBAIaGhvCOd7zj2K/ztd0yvp3IgzHAKTRGCFQ0hoKqYdhnw0/2e840m9yvhguA+qxfIi9D4DhTvyn0zt40G0iI+YrForF2cGVl5VDA0AmCYJxb3N/fj0Ag0BBPLM1EURRcu3YN09PTyGazVdf8fj8ikQhGRkaohYzFbG1tIRaL4ebNm4f6+OmnwgSDQeP9jDF89rOfxdraGrLZLAKBADo6OvD+97//xLvCZ7ZK+O5qAYwBLouHQH3mr98j4d0DXsvvF2ioADi7XcL3VwumzPrVsn82sMsp4p39HlobSIjJGGPY3Nw0AuHa2lrNVjNut7vq3GK73ZrrdZqRpmm4efMm4vH4ocbBLpfLaCFD7X/MwxhDMplEPB7HyspK1TVJkoxNPbVO9Ll27Rqee+45yLIMt9uN97///QgEAvc1hvntMp5P5aEwwG3REFjRGMqqhiGfDe/s81g+/AENEgAZY3hls4QfrhXBYM1XASpjyCsa/DYB7x7woNNJJSZCrKJSqSCRSBiB8GDjYh3Hcejq6qo6t9hqjzXNiDGGlZUVxGIxpFKpqms2m81oIeNyWecUhWbHGMPS0hJisRg2NjaqrjmdTkxMTGBiYuLYF0yqquJb3/oWNjc38cwzzxy56eMkbqQr+HYih7LK4LTIBBCw93kqKAyMA0b9Nryt122ZsR3H8gGQMYYX14p4ZbMEAYDTwgdFa3dDoEfi8VMDXloXSIgFMcaQTqeNUnEymazZasZutxthsK+vjwLIGVhfXzdayOx/ehIEAaOjo5iamoLf7zdxhM1NVVVcv34d09PTh3bde71eTE1NYWxszJR1tJtFBc+lCkhaYAkY8HrJ1yXyeFPIiUtBe0O9YLR0AGSM4eWNEl5aL0LkAEcDNIXUz/zzSDx++pwXHTQTSIilKYqC1dVVIxAePM1iv/b2diMQhkIhWqN2inZ3dxGPx7GwsHCohczQ0BAikQg6OztNHGFzqVQqRiuXQqFQda29vR2RSATnz583vb2SqjHEt8zbBApUz/qd80h4JuyC34J9/o5j6QAY3yrhe6kCeFh75u8gvft3wCbgvYNeBGhNICENI5fLGWEwkUgYZ5ceJEkSwuGwEQj3N7Ml9ZPP5zEzM4MrV64caiHT29uLaDSKcDjcUDMvVlIoFIzP78Hv9Z6eHkSjUfT19Vnu86vPBqbyMhj2JojEExz28DC0fWv+3Q0667efZQPgakHBn93KQGOAq4HCn067OxPY55bwviFvXZpSE0LOlqZph1rN1HrI9Pl8Rqk4HA7T2bd1Vi6XMT8/j9nZWRSLxaprnZ2dmJqawvnz5xv2yfispdNpTE9P4/r161VLIDiOw+DgICKRCLq6ukwc4fFUjeHqbhlz22VsllSjR6+9jv2ADx4G4RJ5jAdsuNzuaMhZv/0sGQBljeFzNzPYKCoN2wUc2OsHVFQ1PNXjwiMddEwVIY2uVCpVtZo5GER0PM8brWb6+vrQ1tbWsI9jVqOvUYvH48hkMlXX/H4/pqamMDo6SuX5GjY2NhCLxbC0tHRojeXIyAimpqbue5eu2RhjSBYUXNkp42amgrLK7vsY2P1/l4a952+F7U3mCByHDqeAy0EHhv1Sw59RrLNkAHxxrYAfrRfhEngIDbKbppaCokHkgZ8970O7g9YDEtIsGGPY3t42TiZZW1urWqu2n8vlqmo143A4zni0zYcxZrSQOdgE3Ol04vLly7h06RK19cHe5yqRSCAejyORSFRdkyTJ2GVdq5VLI8nJGq7vlrGUlbFVUiFre6d3AQCHvTAIAPvzIGOAhr2wt3eNg8gBXolH2C1iLGBHj0tsuhdxlguAeumXscZa91eLvh6QSsGENDdZlpFMJo1AeLDBsY7jOHR2dla1mjF7YX0j0/vUxWKxpg839+u4kKz3WWzWkKxqDNtlFVslFVtlFWsFBZmKBpUx7GayUFUVXo8HDpsIu8Cjyymg0yGi3SGg3SE0RQa5F0sFwGYp/R5EpWBCWgtjDJlMxigXJ5NJKIpy5L12ux3hcNg4maQVg0q9bGxsIB6P49atW4fKm/pJFY1W3nwQiqIYrVwOlsl9Ph8ikUhLl8lnZmbwpS99CQCHcLgHH/3oR80ekiksFQBnt0t4NpFvitLvQQVFg03g8OERf9O/qiCEVFNVFaurq0Yg3N7ernlvMBg01g729PS07JP0w2iGDQ4P4riNMpFIBENDQ00zufIgGGP4oz/6I2MNpNvtxoc+9CF0d3ebPbQzZ5kAyBjDpxcz2Cwq8EjN94CnN4l+S9iNqXZa/0NIK8vn80YYTCQSKJfLR94niiJ6enqMQOj3+1v6yft+FQoFzM7OYn5+vqFanNwvapVzcjdv3sTXv/51oyTucrlw8eJFvOc97zF5ZGfPMgFwJSfji0tZ2HgOUpPN/ulyioYOu4BfuOCjH0RCCIC9F7/r6+tYWVnBysoK1tfXa7aa8Xq9xtrBcDhMZ+SeUKM0Ob5f1Cz7/jDG8NnPfhbJZNI45USSJLS3t+O9731vy80CWiYAfnM5h6u75aZa+3eQojGUNYb3DnrR76EeYYSQw8rlshEGV1ZWkM/nj7yP53mEQiEjELa3tzftY2e9qKqKhYUFxONxyx1zdj/ouLwHc/PmTXzrW9/Czs4OyuUyeJ4HYwzt7e04d+5cy80CWiIAZmUVf7yQBmONcdzbw8jKKsYCdryz32P2UAghFscYw87OjtF3cHV1tea5xU6nE319fcZ/TidtOKuFMYalpSXE43Gsr69XXXM6nZiYmMDExISldscyxrCysoJYLIZUKlV1zWazGbud6bzqo+mzf2tra0in02CMGS+Y7HY7fD5fy80CWiIAvrpRxAurhaae/dOVVA0cB3xoxA9vE651JIScHlmWkUqljEB4cBZrP73VTF9fH0KhUMOVN88CYwypVAqxWAwrKytV1yRJwvj4OCYnJ+HxmPeCXdM0o5XL1tZW1TWXy2W0cqHlAPemz/7t7u7une6hKHv9/kQRiqIgEAi03CygJQLgl5YyuJ2VWyIQsbtHxL1rwIMRv3VeXRJCGs/BVjMHNwDobDab0Wqmr68PXq/3jEdqfZubm4jH47h582ZVWZXneaOFTDAYPLPxKIqCa9euYXp6+lBPyUAggKmpKYyMjNAu8RPYP/uXyWTgdruNpRV2ux2yLEMUxZabBTQ9AGqM4RNXd1FRWcu0R8nKKh7rcuJNIZqqJ4TUh6qqh84triUQCBhrB3t6eiy/5u0sZTIZTE9P49q1a4fK7efOnUM0GkUoFDq1j18ulzE3N4fZ2VmUSqWqa11dXYhEIhgcHGz6alk97Z/9AwC3223MnjscDoiiiFwu13KzgKYHwO2Sij9ZTEPimnf370FZWcU5r4T3DvrMHgohpEkVCgUkEgkjEB4MEzpBEIxzi/v7+xEIBChcACgWi0YLmYNterq7uxGNRtHf31+3z1Uul8PMzAyuXr16aCa3v78fkUgEPT099LW5Twdn/3w+H3ieN8Kgw+GA2+3Gzs5Oy80Cmh4AF9JlfP1O7lTW/1394ffwW7/40/e8559+8ssYf/wpzH3/O/jif/1N3J6fBi+IuPDI4/jZf/AxDE5E6jomACjebQr90fEAePphJoScMsYYNjc3jTC4trZWs9WM2+2uOrfYShshzCDLstFC5uCO7La2NkQiEQwPDz/wGsudnR3E43HcuHHjUCuX8+fPIxqNor29/aH+Da1s/+yfoiiQJAmaphl9IQVBgCRJUFUVmqa11Cyg6QHwxbUCfrRePJX1f0tzcfzBv/5HVe/TVAVLczEAgGiz49988QVsrCzhP/3vvwBNVREMhSFXysjtbMHmdOGf/8lfoH90oq7jkjUGmTH81WE/2hy0foMQcrbK5bJxbvHKygpyudyR93Ech66uLmPtYGdnZ8vOQKmqihs3bmB6eho7OztV1zwej9FCRpJO1uJrdXUV8Xgct2/frnq/IAgYHx/H1NQUrdWsg7/8y7/EwsJC1fc4Y8yYERdFseprZrPZ4HA48Eu/9EtNv7HG9AD45aUMls5wA8infvPX8Bef/G8AgI/+m9/BMx/8CD72vjdj5fo8hiOP4f/6w6+iUi7iX7zvKWwm7iD61nfhV/7bp+o6BtoIQgixCsYYdnd3jc0kqVSqZqsZh8OB3t5eIxC2YssRxhhu376NeDyOtbW1qmsOh8NoIeNwHD7xiTGGO3fuIB6PY3V1teqa3W43WrlQC5/6uXHjBp5//vmqs7gVRakqAR/c5X3u3Dm8853vbPoXO6av/M3J7MzKoC9+5bNG+HvrL3wUz3zwI9hZS2Ll+jwAIPq2d0EQRThFLyaefCue+8wnMf+D56CpKvg67rTSv6mKiukbsAkhLY7jOASDQQSDQUxOTkJRFKRSKaMR9f7ZrlKphMXFRSwuLgLYO0VDLxeHQqGW2JGqnyd87tw5rK6uIhaLYXl5GcDe5+eVV15BPB43Wsh4vV5omoYbN24gHo8fmj10u92YmprC+Pj4iWcPyclduHABQ0NDVeX1zc1NfOlLXwIAjI+P48knnzSu6a1hWoHp/0qFMZxF/Fu+NotPfOxXAAAXHnkcH/q13wIAbKcSxj2+ttePzPG17/26Uioiu70Jf2d9d31x4KCa34GHEEKqiKJobAgB9jYn6KXiRCJRdabu1tYWtra2EI/HIUkSwuGwEQh9vube5MZxHHp6etDT04Pt7W1jHZ/eY252dhazs7Pw+/0olUqHNpIEg0Fj/WArBGczCYJQ9TmWJMmYiNHXALYi0wOgquHUA2A+vYv//Pc+gkqxgEBnN375P30S4jFf8FOvjHMMqnb8bYQQYiaPx4OLFy/i4sWL0DTtUKsZ/bFSlmXcvn3bWNPm9/uNRtThcLipn2Tb2trw1re+FY8++ihmZmYwPz+PbDaLUqlktOORJAkulwt9fX2IRqMYGBho+hIjsTbTA+Bp0zQNv/uP/xY2lpcgSjb88m9/EoGu17d3t/X0Gr/ObG8Yv85u7/3Q2hxOeNs6zm7AhBBiUTzPo7u7G93d3XjsscdQLBarWs0Ui0Xj3nQ6jXQ6jbm5OePP6WsH29ramjb86IH4Xv8+C5y/QIj5AVDggdP8UfjC7/xbzHz3LwEAH/7nv4ULjzxedT0YCqN35CISC1cQ+/bX8VN/8++jUi5i7vvPAgAuPfGWuq7/MzAO+rHHjDHk83mIogi73d60D4yEkObidDpx4cIFXLhwAYwxbG9vY3l5GcvLy1hbWzPWXWmahmQyiWQyiZdeesmYCdNbzRy1YaKR6GXwxcVFI9y5XC643W74fD4Ui0WjdL62toa/+Iu/QDAYxNTUFC5cuEAlYGIK03cBf2ohje2yCvcpnAJy9Ycv4OO/9NNgjEEQRZy7FD10zzMf/AiCoR78p7/zV8E0rboNjMOJX/vUNzAwPln3sWVlFW8JuzHV7sA3v/lN3Lp1C8DeK2yn0wmXy3Xs22YuqRBCGpssy0gkEsbu4oPHmek4jjPOLe7v70dnZ2dDnFvMGDu0CUQniqLRysXj8UDTNCwuLiIej2N7e7vqXrfbbZznS4/pZ2NzcxOf//znAQCXLl3CU089ZfKIzGH6DKBH4rBZOp0Mupm4bbwaUxUFN6dfPnTP5NNvx1t+7m/gV3/3T/Gl//Zx3J6fhiCImHjyrfiZf/DPTyX86WNyihwYY9jYeL30rGka8vn8oYajR5EkCU6n80SBkV5hEkLOkiRJGBwcxODgIBhjyGQyRqk4mUwabTkYY1hfX8f6+jpeffVV2O129Pb2GoHQ7Xab/C+p9iBtYHiex8jICC5cuIDl5WXEYjGjDUw+n8eLL76I1157jdrAkDNl+gzgaTaCtqqDjaA3NjYwPz+PfD6PYrFo/FfPL43dbjfCYK2g6HK54HA4qARNCDlVqqpidXXVCIQHZ8X2CwaDxtrBnp4e017M6o2g4/G40UNO5/V6MTk5eV+NoNfW1hCLxY5sBD02Noapqamm30ltFpoB3GN6ADzNo+Cs6iRHwemdyguFAgqFghEKj/r9wfYCD4PjODgcjhOVoG02W8t8zQghpyefzxul4kQiUfMxTRRF9PT0GIHQ7/ef+mMQHQXXfCgA7jE9AG6XVPzJYhoSx0HiWyNMZGUV57wS3jtYn1d3qqoeGRCPeru/G/rDEgThxCVoWttCCDkJvSSsN6JeX1+vWQ3xer1GqTgcDtf16K5isYjZ2VnMz88fCqTd3d2IRqPo7++vWwDN5XKYmZnB1atXIcty1TW9dUxPTw+96K4DCoB7TA+AGmP4xNVdVFQG5ylsBLGirKzisS4n3hQ6+2OUZFmuOaN48O3+V6MPS++BdZISNK1XJIToyuWyEQZXVlZqro/meR6hUMgIhO3t7Q8UljKZDKanp3Ht2rVDR+KdO3cO0WgUoVB9DwbYr1wuY25uDnNzc1VtdQCgs7MT0WgUg4ODFAQfAgXAPaYHQAD40lIGt8/wPGAzNco5wIwxVCqVewZE/delUunU1ive6y2tVySktTDGsLOzY6wdXF1drXlusdPpNBpR9/X1HbuxYnNzE/F4HDdv3qx6PNM3cExNTSEYDNb133MviqLg2rVrmJ6ePrSD2u/3IxKJYGRkhF4wPwAKgHssEQBf3SjihdVCS6wDLKkaOA740Ii/aQKvpmkolUrHzigWi8W6r1fcHwqPmlHU30/rFQlpPrIsI5VKGYEwnU7XvFdvNdPX14dQKASe58EYQzKZRDwex8rKStX9kiQZrVzM3ImsaRpu3ryJeDyOra2tqmsul8toIVPP8nezowC4xxIBMCur+OOFNBgDHEJzl4GzsoqxgB3v7PeYPRRTqKp6zzWK+9/We73iSUrQTqezZQ4CJ6TZZDIZYzNJMpk8tJZOJ0kSPB4P8vk8SqVS1QYOp9OJy5cv49KlS7DbrVOlYYxhZWUFsVgMqVSq6prNZjNayLhcZ7+0qNFQANxjiQAIAN9czuHqbrmpZwEVjaGsMbx30It+D22KuBf9QPWTlKDrvV7RZrMdO6Oo/7oRGtYS0opUVcXa2poRCLe2tsAYQ7lcRqFQqHrMEAQBHo8Hk5OTePzxxy1/Msn6+jri8TiWlpaqytWCIGB0dBRTU1Pw+/0mjtDaKADusUwAXMnJ+OJSFja+eXcD5xQNHXYBv3DB17Qh1wz7H9TvNaOo/1dP+1vm3Csw0npFQsxTqVQQi8Xw2muvIZfLoVKpGMFJrw7oy0QEQUBPT4+xmSQQCFj2Z3d3dxfxeBwLCwuHWsgMDQ0hEomgs7PTxBFaEwXAPZYJgIwxfHoxg82iAk+TrI3bT2MMeUUzjn8j5tA07dDsYa23+tmd9cBx3KGgWOv3kiRZ9gmHkEZSKBQwMzODK1euHPp5DgaDCAaDyOfz92w143a7jb6Dvb29lioL6/L5vNGy5mDZu7e3F9FoFOFwmB5X7pJlGX/4h38IWZbxlre8BWNjY2YPyRSWCYAAMLtdwrOJPFwCD6HJZgELd5s/f3jE3zLtbhqdoijHzijqJepaOxEfhCiKR25uOWqmkdYrEnJYOp02Zsb2/2xyHIfBwUFEo9GqmbFyuYxkMmlsJsnlckf+vRzHoaurywiEnZ2dlgpV5XIZ8/PzmJ2dPVTt6OjoQCQSwfnz5y01ZrPkcjlkMpmW7q1oqQAoawyfu5nBRlFpqrWAisZQVDU81ePCIx10xmOzYYxV9Vc8LjDW80fOZrMd2tByMDDqLXNovSJpdhsbG4jFYkeujdNbuQQCgXv+HYwx7O7uGmsHU6lUzRd4DocDvb29RiC0ygYMVVVx/fp1xONxZDKZqms+nw9TU1MYGxtr6RYyjDEwoOZpXK3AUgEQAFYLCv7sVgaMoSlmyhhjyCoa+twS3jfkhdDC32zk9SP+TlKCLpVKdfu4+hF/JylB2+32pnnxRZofYwyJRALxeByJRKLqmiRJxu7YB23loigKUqmU0Yh6Z2en5r3t7e3G2sFQKGR6wGKMGS1kNjc3q65ZdbdzPSkaw05ZxVZJxWZJxXpRQbqiQmOAygCAgQMHgQccAocup4gOh4h2h4AOh9AUGeReLBcAAeDFtQJ+tF5silJwQdEg8sDPnveh3UHlOnJyqqoa50EfDIgHZxVrtbt4EDzPHzujqL8VRZHCIjGFpmm4detWzXCj98erd7jJ5XJGqTiRSNRcKyxJEsLhsBEIfb76HP35IPR+h7FY7MiQfPHiRUxOTpra77BecrKG67tlLGVlbJZUKBqDejfmcOAg3H242v+wxRigYW+t/t41DiIHeCUeYbeIsYAdPa7me6yzZABsllIwlX7JWZFl+cSbW+q9XvEkJWin02n6bAhpDoqi4Pr165ienj5U3vT7/ZiamsLo6OiZfL9pmob19XUjEG5sbNS81+/3G42ow+Gwaeejb2xsIB6P49atW0eeeBKJRI4tk1sNYwzJgoL5nTJuZSooq3szeyIPiNxe6DtpjmCMQcPe87fCAJUxiByHDqeAiaAdF/w22JukX7ElAyDweilYY4CrAadhtbtHvlHpl1iJfsTfcTOK+q/rfcTfcTOK+vVGfdFHTs+9Njh0dnYiEolgaGjI1O+dYrGIRCJhBMJabad4nkd3d7exdrCtre3Mx51OpzE9PY3r168f2iijn3nc1dV1pmO6X6rGcHW3jLntMjZKKjTGIPEc7DxXt88nY3tBsKTutdlxiTzGAjZMtjvgtzX2i1rLBkAAiG+V8L1UATwaaz2gvu4vYBPw3kEvAvbG/iYhrUlfr3iSEnS9j/g7qr/iUYGRjvhrfvl83mjl0kgtThhj2NraMjaTrK2t1WxY73K5jFJxb2/vmTaiLhQKRguZg+Xsnp4eRKNR9PX1We7zu1lU8FyqgFR+73vCLvAQ72Om70FojKGkMiiMwS3yeGPIiYlg466ZtnQAZIzh5Y0SXlovQuQa45g4dnfmzyPx+OlzXnQ4ad0faX6qqh5qjVPr9/VcrygIwj2P99v/1qySG3kwxzU5jkaj6OjoMHGE96dSqSCZTBqBMJvNHnkfx3HGucX9/f3o7Ow8kx38lUoFV65cwczMDAqFQtW19vZ2o4WM2d0EVI0hvlXCyxtFlFQGl8BDPOO9AowxFBQGxgHnPBKeCbsacjbQ0gEQ2PtEv7hWxCubJQiw9kyg3uzZI/H4qQEvul0U/gg5SF+veFwJ+uBxXQ9LkqQTl6BpvaJ51tbWEI/Hcfv27aY95owxhkwmg+XlZaPVTK2zz+12O3p7e41AeNobNVRVxcLCAuLxONLpdNU1r9drtJAxowepPuuXzMsQOA5OoX6l3gchawwlVYOrQWcDLR8Agb0fllc3S3hprQgGwGXyF/0oqsZQUDX4bALePeBBJ838EfJQ9PWKx80oFgoFlEqluq9XPEkJmo74qw/GGFZWVhCLxZBKpaqu2Ww2XLp0CZOTk3A6m3MznaqqWF1dNdYObm9v17w3GAwaawd7enpO7cUKYwxLS0uIx+NYX1+vuuZwOHD58mVMTEwcu8taVVV861vfwubmJp555hn09/c/0HgW0mU8m8ijrDI4TZj1q2X/bOCo34a39botM7bjNEQA1M1ul/D91YJp075HYfvWBHQ5Rfxkv4fW/BFyxjRNq+qveK/AWO/1iictQdN6xcM0TcPi4iKmp6extbVVdc3tduPy5cu4ePEibDabSSM0Rz6fN0rFiUSi5vesKIro6elBf3+/0Wqm3t9jjDGkUinEYjGsrKxUXZMkCePj45icnITH4znyz1+7dg3PPfccZFmGy+XCBz7wgfveZTy/XcZzqTxUBrgtOAEEABWNoaxqGPLZ8M4+D2yC9cZ4UEMFQADYKil4LllAwgJTwPqsn8RziHY48IZOJyQLhFJCSG36esWjStAHg2OtstyDEAThyOP8avVXbGaKouDatWuYnp4+tBYuEAhgamoKIyMjVIrHXgBbX183AuHGxkbN2W6v12uUisPhcN2D89bWFmKxGG7evHmohcyFCxcQiUQQDAarxv7Zz34Wa2tryGazCAQCaG9vx/vf//4Tj21mq4TvrhbAmDWrf/vpJeF+j4R3D3gtHwIbLgACe315prdK+NG6OYtAD876vSXspvV+hDQZxhgURTlRCbpYLNZ9veJJStBOp9P0Rfn3o1QqYW5uDnNzc4dOuunq6kIkEsHg4KCln+TNViqVkEgkjEB4cMOGjud5hEIhIxC2t7fX7fOayWQwPT2Na9euHeorqreQCYVCuH37Nr7xjW9gd3cXiqJAEAQEAgEMDQ3hHe94x7Hjubq7V/ZlDdQOTrk7MTTss+En+z2WqFTW0pABUKfPBiYLChhjsPEcbHXs/3OQyhjK6l5XcZr1I4ToGGMol8snKkHXe72ifsTfcSVoM9cr5nI5zMzM4OrVq4d2gff39yMajaK7u5uC331ijGFnZ8dYO7i6ulqz0bvT6TQaUff19dVlPWWxWDRayBwsU4dCIeRyOezu7iKdTsPtdqNQKECSJPh8Pjz22GN45JFHav7dS9kKvn4nB1UDXKK1Z/4O0mcCxwN2/ESf27Jjb+gACOztvL2TlTG3U8ZyTkZF2+vabRe4ujRfZoxBZkD5bhNIj8TjYtCOsYAdQVrrRwi5T/p6xZOUoGsdM/YgOI47cQlakqS6PGnt7OwgFothcXHxUCuX4eFhRCIRtLe3P/THIXtkWUYqlTIC4cFdvPvprWb6+voQCoUeaiZZlmVcvXoV09PTyOfzxvvS6TQ0TYMgCGhra4Msy8hkMnC5XHC73XjXu9515KaQgqLhM4sZZCpqw54GVlEZKozh7b1uXAxa86zlhg+A++2WVVzdLePKThk5ee/B5n6Pg9EYg6IBCnv9/ECB4xByiZgI2jHst9GMHyHkTCiKcmQwPOr39TziT1+vqO90vlcJ+qj1iqurq0Yrl/1EUcTY2Bimpqbg9XrrNl5ytEwmY5SKk8lkzR6cNpsN4XDY2F38oF8bVVVx48YNxONx3LhxA6qqGgFQFEU4nU5omoZisQifzwev14sPfOADVeckM8bwrZU8ruyW4RF58A0Y/nQ5RYNL5PBz533wWrBPYFMFQJ2sMdzKVLBeVLFWVLB14EBoAGAAuCN+DQAiv7e5JOQS0ekQ0eeREHIKDfkqhBDS/BhjVf0VjwuM9XzYt9lsxqyiLMvY3t5GPp8Hz/PGfw6HA5OTk7h8+XLTtnKxOlVVsba2ZswOHtx1vV8gEDDWDvb09Nz3pqQXXngBzz77rPGiRBCEqhlGjuPAGEMwGERnZyfe9773GY3aF9MVfH05B5FDw5+5q58KNuyz4d0DHstliKYMgAcpGsN2WcVWScV2WUVFZXcPembgAEj8XrnYI/FodwhodwjwSo057UwIIfeiH/F3VEA8qr/iSf4+ff3jwVlInueN9Yc8z1f1V7zXW7u9sRrqNqJCoVDVaqbW11oQBPT09BiBMBAIHPu1+d3f/V1sbGxAVVXjRQDwegWOMQZVVY3S8OjoKN72trehqDJ8ZjGDbEWFR7LejNmDkDWGsmbNUnBLBEBCCCH3Ty/XHTWjmMvlkEqlsLm5CVmWD53a4XQ6HzjI6cHxJCXoeq1XbGWMMWxubhonk6yvr9ecJXa73UapuLe391Aj6K2tLfzu7/4uFGVvcybHccYMoCAIRhmaMQZN04y1qU8++STy/ZOY32n80u9BRil42AevhYItBUBCCCEnViqVMDs7i7m5uaqdn4wxtLe3Y3h4GH6//56Nueu5XlFfW3aSmUXqK3gy5XIZyWTSKBfncrkj7+M4Dl1dXUYg7OzsxFe+8hXE4/Eje2hyHAee58EYO9RHUPAG4X3rz0MShIYv/R7EGENO0fB4lxNvDLnMHo6BAiAhhJBjZbNZo/fbwSf3gYEBo5XLcfQj/k5Sgi4Wi3Vfr3iSoKiXrMne12t3d9coF6dSqZoB3uFwYGNjwzjHW/8c6jN9PL+3tMrj8UAURZTLZaMtEjcchXP8MQTszTmjm1c0uEUOHx4NWGYjKQVAQgghNW1tbSEej2NxcfHQrI3eyqWtre1UPra+XrHWppaD/RXrheM4o/R8XAm61dYrKoqCVCplBMLd3V3jGmMMW1tbRvlXL/vyPG+8z263Q5IkDA8Po6OjAwCwncnidmgSnORomIbP90tlDAVFw0/2ezAasMZaQAqAhBBCqjDGsLq6ilgshuXl5aproihifHwcU1NTNc9/NYOqqkZYPG4XdK12KA9i/3rFk/RXbDbZbBYrKytYWVnB0tIS1tfXoaqqsfZvP7306/V64XK58NRTT+HixYu4ulPGN1dycIl8Xfr3WlVWVtHnlvD+Ia8lXjRQACSEEAJg7wn69u3biMfjWFtbq7rmcDgwMTGBiYkJOBwOk0ZYH3rLnJOUoOu9XvEkQdHpdDbkesXPfOYzuH79urHD92DI0Td+MMYgiiIcDgeijzyC7YEfQ6qgWGqDxGmoqAwKGD543ocup/nHx5o/AkIIIaba38B3f0kPALxeLyYnJzE2NtY0M1iSJBlHkt2Lvl7xJCXok6xXVBQFmUwGmUzm2DHqLXOOK0E7nU5LzCYBwMrKihHwgL01lzabDZVKBeVyuerzoygK8vk8fjB9BW7/OFwN/qLiJCQeKCsMC+kKBUBCCCHmkWUZV65cwczMjHGEl66trQ2RSATDw8MtuyGC4zjY7XbY7XYEg8F73rt/veJxJeiD5+YepVwun+i+/esVj5tZtNlspxYW9VlVPeSpqmr82/XNH/rb/S1gOF87VHCQrJFhTxXHceDAIZU/vEPaDBQACSGkxRSLRczOzmJ+fv5QyOjp6UEkEkF/f79lZpYaAcdxxozccecbq6p6zxnF/W+PaqeyH2PM+LuOo/dnPEkJ+n5ne/Vj//QAqL/VXzxIkgSv1wtBECBJEgKBACRJQq5nDBst1MtR5IHtsgJZY6bvBqYASAghLSKTyRitXA6ubRscHEQkEkEoFDJpdK1DEAR4PJ4TbaKRZflEJWi99cq9qKqKXC5Xs6/ffpIknbgELQgCtre3Adyd5bo726eXg/XdwJqm4fLly3jiiSeM4+U+s5gGV6jfOkurEzkOFY1hu6Qi5DI3glEAJISQJre5uYl4PI6bN28eauUyMjKCqampY0ucxBySJMHv98Pv99/zvv3rFY8rQeu99+5FlmWk02mk0+ljx2i326vCp37snyRJ0DQNpVIJ5XIZgiDgypUrSCaTePrpp9HZ3YOdsgozO7+89JXP4au//ztI3bwOm8OBi298Bj/3D38dXQNDp/LxBG6vJcxW2fwASLuACSGkCTHGkEwmEY/HsbKyUnVNkiRcvHgRk5OTcLvdJo2QmEUPZceVoIvF4onWIQJ7J8Rks9lD5/8C1bt/OY6DJEkQBAFdFy4hN/Im2Hkeognl0Oc/+wf4xMf+PgCgs+8ccrvbKOay8LV34l9/4bvwd57ObHhWVhHtcODpHnN/9mgGkBBCmghjDLdu3UI8HsfGxkbVNafTicuXL+PSpUuHznAlrYPneaOse5L1ivezC/qoOaWDJeFKpQKO45DYzsBeluFy1f978ZO//qv4zqf/BwYuTuJfff554/2/8dd/CguvvIgf+4m/guuv/AAA8Og734tf/u1PYmc9hX/27jcis7WBP/+9/4gP/9pv1X1cAMCBw3rR/LI3BUBCCGkCqqri+vXrmJ6ePlS28/l8mJqawujoqLH2ipCTEAQBXq8XXq/3nvf90R/9EXK5HDiOg81mM15gyLIMWZahqqoxA2isE7TZIQj8qWwAefP7/hq+8+n/gTtXZpC6tYCeoRFsryZw49WXAACDExG8+q0/BwC84Z0/DQAIdvVgOPIo5r7/LGa++5d1H5OO54Cyan7xlR4JCCGkgVUqFczPz2N2dhaFQqHqWnt7O6LRKIaGhlq2lQs5G9vb21UzfJVKxbimh0j9jOVgMIjh4WFUQsN4eft0WqJceORxdA9ewOrSDfzwq1/A+375n+BHX/8iGGMIdPWgo3fAuNfX1vn6r9v3fr2VWjn0d9aTolEAJIQQ8gAKhQJmZmZw5cqVqidbAAiHw4hGo+jt7W2Z9hrEOvQd5nrpV//10NAQotEourq6wHEcfrReBHB6PfGefN8v4PO//W/xw6/tBcAffu0LAIAnfvrnwPNHnzrCcPrBjMPeRhCzUQAkhJAGkk6nEY/HsbCwUNXKheM4DA4OIhqNorOz8x5/AyGn6+CLDkmSYLPZcPv2bZRKJYyPj+P8+fM47dcmT773F/CF3/kNJBevIvbs13Bz+mUAwJvf/9dQyOwa92W2X18rm93aBAC09/Sd2rgYAN4CL8woABJCSAPY2NhALBbD0tJS1UJ7QRAwMjKCSCRybKsQQk6LdLeZM8dxxjm/oigax8CVy2WIoohsNoubN2+C4zh4Lz4KFr54amNqD/dj/I1P48qLz+N//ItfBQAMTf4Yei+MQ6lU4Am0Ibe7jVf+4st403s+iJ31FBbjeyFx8um3n9q4AEA0P/9RACSEEKtijCGRSCAWiyGZTFZds9lsRisXl8tl0ghJK9KPfdu/G9jn82FnZweapoHneVQqFZRKJePPMMaqjrfjeR6FVBL2jguAdHQ5th7e/L6/hisvPo/05trd3/9VAIBos+Fnf/Vj+OS//FW8/Bdfwj95RxS53W2U8ll4gu1499/+B6c2JgZAMPkUEIACICGEWI6maUYrl83NzaprLpfLaOVis9lMGiFpNqqqHjrLuNaJI7IsH/rzxWLRaAatKErVpiO9JKyfA2zsApZLEEUeGmOnVhJ99J0/jT/8N/8YpUIOomTDG9/zQePaj//8L8HudOHrn/gvSC5eh2S34w3v+Gl88P/4lwh29ZzKeABAYwxB2+mF3pOiRtCEEGIRiqIYrVwymUzVNb/fb7RyEQTznzyI9THGjFBX66xh/e1JGz7XUi6Xkc1mjfAnCAJ4fq/Fi6ZpUBTFOC/Zbrejr68Pj775GXxpjUEAB5tg/ozYWcnKKt7c7cKPdTpNHQfNABJCiMnK5bLRyqVYLFZd6+zsRCQSwdDQEO3oJUablXs1Zt4/Y1fPOR673W6c/3vwTOBisYjvf//7yGQysNls8Hg8kGUZuVwOmqbB7XbD5XLB4/HgySefxODgIADAtZVGXtZgQ2t8b2t3Z0DbHOa/iKMASAghJsnn80Yrl4Nltd7eXkSjUYTDYQp+LUBRlBMFukKhULX7+2GJonhkoNNPCtkf+O4185xOp/HDH/4QgiBAVVXkcjmUSiWIoohAIABJknDp0iU89thjVUsXQk4BCxXzT8U4K4q2twGkw2F+/DJ/BIQQ0mJ2d3eNVi76uilgb43U+fPnEYlE0NHRYeIIST1omnZkgDsq4B21ru5B8TxfFebuFfBEUazLCwx9IxLP8yiXy1AUBR6PBw6HAx0dHXj66aePbE/U6RSxkK4cen+zUhiDW+LhtsA2YAqAhBByRtbW1hCPx3H79u1DrVxGR0cRiUTg8/lMHCE5jr6u7rizcQuFQtUu2HrQw9xxM3Z2u/3MZ40lSYIkSeB53igB2+12PProo5iYmKh5Ek27QwDHcVAZg9ACM90qY+h2CpaY1acASAghp4gxhuXlZcTjcaRSqaprdrsdly5dwuXLl+F0mrsgvJUxxqpamxxXgq3nujqbzVYz0B2cxbP6cX6BQACVSsVoSv7mN78Zbrf7nn8m7JLgFDiUVQaXBWbFTpN++sc5rzV271MAJISQU6BpGhYXFzE9PY2tra2qa263G5OTk7h48SIkSTJphM1v/7q6o2bs9l9TlPodSSYIwpFr6I56K4rN8zT89NNP4+rVqxgYGMDAwMDxfwCATeBwMWjHKxtFo0VMsyqrDB6Jx7DfGgGQ2sAQQkgdKYqCa9euYXp6GtlstupaIBBAJBLBhQsXqJXLA9I07VBrk1ozdQfPSH4YHMedKNC5XC7jVAxyMtslFZ9eTIMHYBesPcv5oBhjyCkaHuty4k0hazRub56XHoQQYqJSqYS5uTnMzc0dWvvV1dWFaDSKc+fOUTA4gn5KRK0Zuv2/L5VKdS3BOhyOE5VgHQ4Hfe1OSZtDQJ9bwlK2AnuTvi6qaAwSz2E8YDd7KAYKgIQQ8hByuRymp6dx9erVQ2XE/v5+RKNRdHd3t1x4YIxBUZQj19MdVYLdvxv6YUmSdGi27qhg53A4aCbWIi4F7bidk6FqzBLHpNVbRWM477MhYKGESwGQEEIewM7ODmKxGBYXFw+1chkeHkYkEkF7e7uJIzwdqqqeKNAVCoW6r6s76WYJWlfZeAa9EgI2HjtlFR6Ob6oXTBV176i7iTbrzP4BLRYAGWNIVzRUNAZVY1AZwHGAwHEQOMAj8XCKzbn+gBBSH6urq4jFYrhz507V+0VRxNjYGKampuD1ek0a3YNhjN1zk8T+3z/skWH76UeDnSTY2Wy2pgoFpJrAc3iqx4Wv3s6hojHYm+RoOI0xlDQNY347znms9cKkaQOgHvY2Syq2SgrWiyo2SgrKKoN2d/mIvoqEu/sfzwFeG49up4gOh4h2h4B2h0ChkJAWxxjDnTt3EIvFsLa2VnXNbrdjYmICExMTlmrloh8ZdpISbL3X1dnt9hOXYCnUEd2g14aLQRtmtsuQ+L1Zs0ZXUBm8Eo+nelyW+15vugCYqai4tlvB/E4ZBUWDor3+oCZyHESeA8ftzfwZ2F4YVBnDblnDdqkMoAyO4yBxQLdLxKU2O4a8NohNuDaBEHI0TdNw48YNxONx7OzsVF3zeDyYnJzE+Pj4mZYc9/eru1cJtlgs1vXIMEmSjg10+nVaV0ce1BMhF1ZyCnYrKjxiY5eCKyoDB+DN3S64JetNJDVFGxjGGJbzCq7ulHEzU4Gs7b1ysPF7pd0HeRXBGIMGQNEYKhoDBw5eG49LQTvGAjb4bPQAR0izkmUZV69exczMDHK5XNW1YDBotHKpV2NefV3dUc2HDwa8eh8ZdlSAOyrg0bo6claWMhV89U4OAte4bWG0u21fxvx2vLPfbckg29ABkDGGG5kKXl4vYbusQmUMNp6Dnefq/slWGUNJZdDY3lbuIZ8Nb+xyWmpHDyHk4RSLRaOVy8G1bqFQCNFoFAMDAyd6fNGPDDtJCbbe6+ocDseJSrC0ro5Y1bOJHGa3y3AKfMNV3vSef16Jx88N+y05+wc0cADMyRq+l8pjMSNDY+zMvkkYYyjfnRV0iTze2OXE5bazP3eREFI/2WwW09PTuHbt2qGdq+fOnUMkEkF3d7exrq7WTN3BEmy919UdFegO/t7hcFj+yDBCjiNrDF+7k8NStgJXA4VAPfw5RR7vOedBj8u6M+cNFwAZY1hIV/DCagFZWYOD52EzYbcQYwwFlYEx4JxXwtM9LpoNJKTBbG1tIR6P48aNG1BVdW/ph6aBMYb29najjcv+YFfPdXWiKJ64BEvr6kirKasa/vx2Dom83BAhUA9/doHDuwa8GLDYrt+DGioA5mUN30sVcCNTAQPgEjjTdwnJGkNR1Wg2kBCL0TTtyJm5fD6PjY0NpFIpZLNZI/Dp9JMhHjRw8Tx/z0C3//eiKNLjBSH3UFQ0fO1ODisWD4F6+HMIPN7R78ag1xrn/d5LwwTA3bKKr97JYrOkmjbrV4s+GwgAk212PNXjMj2YEtKM9HV1JynBHjyObX/59mCZV+9HV6t8qq+rO0kJ1m6nF4GE1FNZ1fAXy3ksZSuQTmmd/8NQNYa8qsEt8njXgAe9bmvP/OkaIgBulRR89XYOO3e3hVs1XJXVvSbTF4N2vDXsbsrjbAipN8YYZFk+0Zq6QqFw3+vq9p8ze7B8K4oi2tra0NnZCbfbXVV2PdivjtbVEWIeWWP4wWoBcztlKBqD2wJZgN3dHKowhi6niLf2utHlbJzuepYPgNslFV++nUWmolriC36cispQ1jSMB+x4e5/b8uMl5LQoinLPtib6DtlCoVDXdXWCIMDlcsFut6NYLGJ7exuqqoLneeO/trY2RKNRjI6OUrAjpIHcycl4PpnHTlk1dTZQ1RgKqgaJ5/BIhwM/1umE1GCTPpYOgOmKii8vZbFdtvbM30F6CJxqd+AZC3b/JuRB6evqTlKCrVQqdfu4HMedaE2d0+mEoihGK5eDY+jp6UE0GkVfXx/9XBLSoMqqhpfWisZs4Fm2itHuzvqpd2f93hJ2o9vVOLN++1k2AFZUhs/fymC9qDRU+NOVVQ0yY3gi5MIbOq1zPBQhB+kl0pOUYOt9ZNhR/eqOCngnOTIsk8kgHo/j+vXrh2YUBwcHEYlEEAqF6jZ2Qoi57uRkfC+Vx05Zg3a3D7DtlGYEFY2hpDEwxuAQeUy12Rty1m8/ywbAF1J5vLpZgkvkITRY+NMVFA0CD3xgyNdQ6wJI49PX1Z1kTV2xWISmaXX72Dab7dhAp/+6HuXXzc1NxONx3Lx5syqc8jyPkZERRCIRBAKBh/44hBDr0RjDck7GlZ0ylrKycRKY/e5JYA8TBjW21/NX/zuDdh4TbQ6M+m1wio2/dMSSATCRl/GlpSw4AI4GPQYGeH1beLdLxAeGfJbdvk4ah6qqx87U6W8P7nR9GPq6upOUYEXx9F/sMMaQTCYRi8WQSCSqrkmShIsXL2JychJut/vUx0IIsYZ0RcW13Qqu7JSRk/dmBQGABweRBwTu6FDIGAMDoGiAwvbKuzobz2HQa8PFNjv63c3VtslyAbCiMnz2ZhpbpcY/CBrYmzYuqhqeCLnwaBeVgslhmqbVPDLs4IxdvY8MO6pf3VEBzypHhjHGcOvWLcTjcWxsbFRdczqduHz5Mi5dugS73W7SCAkhZlM0hvWigq2Sis2SgtWiikxFhaLtHet68JGMAeAAiDwHh8Ah5BTR6RTR4RDQ6RThsehRbg/LcgGwGUq/BxUVDTyVglvK/tYjx5VgT2Nd3UlKsCdZV2cVqqri+vXrmJ6eRjqdrrrm8/kwNTWF0dHRM5l9JIQ0nrKqYaukIidrUNleSGQABG4v+Nl4Dm0OAT6p8SeeTspSAXC1oOALtzINX/o9SC8F97gl/OyQt2W+uZrRSfrVnca6OkmSTlSCdTgcTXVkWKVSwfz8PGZnZ1EoFKqudXR0IBKJYGhoiFq5EELIfbLUy+W57RJkjcHbBIsr9+M4Dg6Bx1pBQSKvoM/i5wO2GlVVTxToisUiZFmu28cVBOHEJVhJaq3vmUKhgJmZGVy5cuVQK5dwOIxoNIre3l56MUUIIQ/IMgEwL2tYzFRObQu32UQOKDGGK7tlCoBngDF2bL86/densa7uJCVYq6yrs5J0Oo14PI6FhYWqVi4cx2FwcBDRaBSdnZ0mjpAQQpqDZQLg9XQZZZXB02SzfzqO4yDxHG5mKsjKKrxS85Tpzop+lutJSrD1Xldnt9trztQdLMFSqLt/GxsbiMViWFpaqvq6CYJgtHLx+/0mjpAQQpqLJQKgyhjmtsvg0Jyzfzo7zyGnaLi+W6Hm0Psc169u/4xdPY8ME0XxyCB31IxdM62rswrGGBKJBGKxGJLJZNU1m81mtHJxuVwmjZAQQpqXJQLgnayM3YoGh9C84Q/YmwXkwWF+p4xouwNCE/cF1NfVnaQEW891dTzPn2hNncvlarl1dVahaZrRymVzc7PqmsvlMlq52Gw2k0ZICCHNzxIB8GamAo0xiC2wk88ucMhUNKQKjbcZhDF2on51p7Gu7l5Hhu0PeLSuzroURTFauWQymaprfr/faOVCs62EEHL6LBEAVwtK0/T8O47A7R0vs1VSLREA9XV1Jy3B1ntd3XGBTj8Hltp8NK5yuWy0cikWi1XXOjs7jVYuFNwJIeTsmB4Ai4qGrKxBbJHHfv1JbrNUv2O6jqIoihHejivBnta6uuNKsDTT09zy+Tymp6dx9erVQ2X+vr4+RCIRhMNhCn6EEGIC0wPgVkmFwgBnndf/Xf3h9/Bbv/jT97znn37yy/AG2/H53/63uDnzKnbXUwCAd/+tX8HP/cNfr+t49uM5DquF+w+AmqadaKbuNNbV3atf3f6AJ4rNdVYiuX87OzuYnp7GwsJCVTNsjuNw/vx5RCIRdHR0mDhCQgghlgiAjDHwh07nezgOtxfnpx6tep+mKliaiwEARJsdga4eJG9cxWvf/iq6h0aMAHjaRA7IyhqKiganyIMxht3dXeTz+XsGu1KpVLcx6OvqTlKCtdvtFOrIsdbW1hCLxXD79u2q9wuCgLGxMUxNTcHn85k0OkIIIfuZHgD1Umi9A8bgRAQf+/Q3q973qd/8NSMAfuRj/x7dg8PwtXfgv/5wCU6PDx+9GKzrGGoReQ5FVV8HyOPb3/42FhcX6/J322y2E5VgnU4nrasjD40xhuXlZcTjcaRS1S+g7HY7Ll26hMuXL8PppLZHhBBiJRYIgGrdZ/+O8uJXPou/+OR/AwC89Rc+imc++BEAgMt79s1leew9ce5WVPQyEaurq/e8XxCEI9fQHfVWFE3/kpIWoGkaFhcXEY/Hsb29XXXN7XZjcnISFy9epFY7hBBiUaanBUVjOO3q4vK1WXziY78CALjwyOP40K/91ul+wGPos52qtvfrn/iJn8Di4uKhxsT6ryVJohIssQRFUXD16lXMzMwgm81WXQsEAohEIrhw4QJt8CGEEIszPwDWr6vIkfLpXfznv/cRVIoFBDq78cv/6ZMQLTArwQFQ7rZUCYVCCIVC5g6IkHsolUqYm5vD3NzcobWooVAIkUgE586doxcqhBDSIEwPgBpjp1YA1jQNv/uP/xY2lpcgSjb88m9/EoGu7lP6aPdPO+XwS8jDyuVyRisXRaneud7f349oNIru7m4KfoQQ0mBMD4A8x4HhdJLQF37n32Lmu38JAPjwP/8tXHjk8VP5OA+qyU++Iw1se3sb8Xgci4uLh1q5DA8PIxKJoL293cQREkIIeRgcq+fRDg/gj67vIl3R4BLruyP16g9fwMd/6afBGIMgijh3KXronmc++BH0jV7C7/2T/xUAsH7nFgDA5fPDE2hDMBTG//k//7yu49JlZRXPhN2ItDtO5e8n5EGsrq4iFovhzp07Ve8XRdFo5eL1ek0aHSGEkHoxfQbQJnA4jQi6mbhtHFumKgpuTr986J7Jp9+O0LnzRvDTFTJpFDJpqMrpnNahj6tVTj8h1sYYw507dxCLxbC2tlZ1zW63Y2JiAhMTE9TKhRBCmojpM4DPJnKY3S7DK7XOrkFVYyhpDB8Y8iLsNn9DCmlNqqoarVx2dnaqrnk8HkxOTmJ8fJxauRBCSBMyfQaw3SECKIMx1jILyRXGIHJAm6N1Qi+xDlmWcfXqVUxPTyOfz1ddCwaDRisXahROCCHNy/QA2OEQwHMcNNY6myIUBrTZBTgEeoIlZ6dYLBqtXMrlctW1UCiEaDSKgYGBlnkhRgghrcz0ANjmECBye7NiwhmcCGIFGmPodtHsHzkb2WwW09PTuHbt2qFWLufOnUMkEkF3t3XaIxFCCDl9pgdAh8DDZxOwXVZhN3swZ0BfcrlX+ibk9GxtbRmtXPYv9eV5HsPDw4hGowgGz+b8a0IIIdZiiRTS4xKwUTqdHbdWo7K93ocdtP6PnALGGFKpFOLxOJaXl6uuSZKE8fFxTE5OwuPxmDRCQgghVmCJAHjeb8fcTgWKxiDyzV0GLmkMbXYe3S5LfOpJk2CMYWlpCfF4HOvr61XXHA6H0crF4aC+k4QQQiwSAPvdItrsArbKKjxNHAA1xsAYw0SbAzwttCd1oKoqFhYWMD09jd3d3aprXq8XU1NTGBsbgyha4kedEEKIRVjiWYHjOEy02fFcMg+NsaYNR2WVwS5wGPXbzB4KaXCVSgVXrlzB7OzsoVYubW1tiEajOH/+PLVyIYQQciRLBEAAGPHb8NJ6EWWVwdmER2QwxiAzhot+B5x1PvaOtI5isYiZmRnMz8+jUqlUXevp6UE0GkVfXx+1ciGEEHJPlgmATpHHqN+G+FYJjiZsCi0zQOQ4jAdbYa8zqbdMJoN4PI7r169DVVXj/RzH4dy5c4hGo+jq6jJxhIQQQhqJZQIgAEy02XF1t4xSk80CMsZQVjUMeCSEnLT7l5zc5uYmYrEYbt26daiVy8jICCKRCAKBgHkDJIQQ0pAsFQA7HCJ+rMOJF9cKTbUjuKAyOEQez4TdTTezSeqPMYZkMolYLIZEIlF1TZIkXLx4EZOTk3C73SaNkBBCSKOzVAAEgEc6HFjKVrBaUODh+IYPTIq2t/P38S4Xgnaa/SO1McZw69YtxONxbGxsVF1zOp24fPkyLl26BLudlhEQQgh5OBzbX1eyiPWigi/cykDT0NAbJhhjyCka+j0S3jvobdrdzeThqKqK69evY3p6Gul0uuqaz+fD1NQURkdHqZULIYSQurHkM0qXszlKwXrp9y1hN4U/ckilUsH8/DxmZmZQLBarrnV0dCASiWBoaIhauRBCCKk7SwZAYK8UvJyTsZKX4eZ4CA0WoEqqBgB4U5eTSr+kSqFQMFq5yLJcda23txeRSAS9vb0Nv/yBEEKIdVmyBKwryBq+dDuL9aICj8g3zCxaWdUgM+CxTgce73LSEzkBAKTTacTjcSwsLBxq5TI4OIhoNIrO/6+9e4+NKy3vOP59z5mbx3Z8ix0nceIo98SXmZbLlrJbetntIhZQ1VK1VIKKClSoUCkIURUKRULqLlUreqGFCqktVCpq6R9ctNsCrdpdQHSXLZ2Z2Ilz3STOxrEd2/H6Mrdzzts/vDY7m2XDLrbPmZnfR4oUzTuWnhlF8i/P+77P6e0NsUIREWkWkQ6AAEsVn69eXmK+7JOORb8TWPIDvMAy2pPint1phT9hZmaGfD7P5cuXa0a5uK67Mcqlo6MjxApFRKTZRD4AAixWfP7tyjIzJY9W18GN6JnAohfgA9meJK/tV/hrZtZarl27Rj6f5/r16zVriURiY5RLOp0OqUIREWlmdREAAVaqAY9cXeLGqkfcMSQdE5mAFVjLqhfgGMOr+lp4ZW8qMrXJ9gqCgKeeeopcLsfc3FzNWjqdZmRkhBMnTpBI6HnQIiISnroJgLB2tu7x6SLjC2W8wEZiS7jiW0pBQEfC5e7+NAd3xBX+mpDneZw9e5ZCocDS0lLNWkdHx8YoF9fVhSAREQlfXQXAdZPLVR67vsJ82Q+tG7je9TPGcLQjwWt3p0nX8cxCeXnK5TKnT59mbGzstlEuvb29ZLNZDhw4oP8UiIhIpNRlAITabmA1sKQch7jDlv+iDayl7Fuq1qrr18RWVlYoFApMTEzcNsplYGCATCbDnj179O9CREQiqW4D4LrJ5Srfny1yfdXDCyyuMaRcs6kjY6y1eHYtdFrWnk5yrDPBK3pb1PVrMgsLC+TzeS5cuEAQBBuvG2M4ePAgmUyGnTt3hlihiIjIndV9AFx3s+gxcavMxK0KRS/AAHHHEDMGx7z0zuB66PMCi2ctjjHsTLkMdSc53JEg5Sr4NZPp6WlyuRxXrlyped11XY4dO8bo6Cg7duwIqToREZGXpmEC4LqyH3BhscLphTLzJR/Prm3bAjjGEHs2DBrAAOsf3lrw7VrYW+c6hoRjGGyPc6Izyd7WmLb0moi1lsnJSXK5HDdu3KhZSyaTnDx5kuHhYVpaWkKqUERE5OVpuAD4XCU/YK7kM1fyuVnymF71eabis75xZy1g1oIgrHUMe1MufS0xelIuPSmXzqQb+k1j2V5BEHDx4kXy+Tzz8/M1a62trRujXOLxeEgVioiI/HgaOgC+kLIfUAksfrDW8TMYXAdcY2iJGYW9JlatVjdGuSwvL9esdXZ2kslkOHz4sEa5iIhI3Wu6ACjyfKVSifHxccbHxymVSjVru3btIpPJMDg4qO1/ERFpGLGwCxAJy9LSEqdOnWJiYgLP82rW9u3bRzabpb+/X8FPREQajgKgNJ35+fmNUS7PbYAbYzh06BCZTIaenp4QKxQREdlaCoDSNG7cuEEul+Pq1as1r8disY1RLu3t7SFVJyIisn0UAKWhWWu5evUquVyO6enpmrVkMsnQ0BBDQ0Ma5SIiIk1FAVAaku/7XLhwgUKhwMLCQs1aW1sbIyMjHD9+XKNcRESkKSkASkOpVqtMTExQKBRYWVmpWevq6toY5eI4epKLiIg0LwVAaQjFYpGxsTFOnz5NuVyuWevv7yeTybB//37d6BUREUEBUOrc0tIShUKBiYkJfN+vWRscHCSTydDf3x9SdSIiItGkACh1aW5ujlwux6VLl2pGuTiOw+HDh8lkMnR1dYVYoYiISHQpAErdsNYyNTVFPp9ncnKyZi0ej3P8+HFGRkZoa2sLqUIREZH6oAAokWet5fLly+TzeWZmZmrWUqkUw8PDDA0NkUwmQ6pQRESkvigASmT5vs/58+fJ5/MsLi7WrLW3tzM6OsqxY8eIxfTPWERE5KXQb06JnEqlwpkzZxgbG7ttlEtPTw+ZTIaDBw9qlIuIiMjLpAAokbG6uroxyqVSqdSs7d69m2w2y8DAgEa5iIiI/JgUACV0i4uLFAoFzp07VzPKxRjD4OAg2WyWvr6+ECsUERFpLAqAEprZ2Vny+TxPPfXUbaNcjhw5QiaTobOzM7wCRUREGpQCoGwray3Xr18nl8vx9NNP16zF43FOnDjByMgIra2tIVUoIiLS+BQAZVtYa7l06RKFQoHZ2dmatZaWFoaHhzl58qRGuYiIiGwDBUDZUr7vc+7cOQqFwm2jXHbs2MHo6ChHjx7VKBcREZFtpN+6siXK5TKnT59mbGyMYrFYs7Zz586NUS660SsiIrL9FABlU62srGyMcqlWqzVre/fuJZPJsHfvXgU/ERGRECkAyqa4desW+XyeCxcu3DbK5cCBA2SzWXp7e0OsUERERNYpAMqPZWZmhnw+z+XLl2tGubiuy9GjRxkdHaWjoyPECkVEROT5FADlJbPWcu3aNXK5HFNTUzVriURiY5RLOp0OqUIRERF5MQqA8iMLgoBLly6Rz+eZm5urWUun04yMjHDixAkSiURIFYqIiMiPQgFQ7sjzPM6ePUuhUGBpaalmraOjg0wmw5EjR3BdN6QKRURE5KVQAJQfqlwuMz4+zvj4+G2jXHp7e8lmsxw4cEA3ekVEROqMAqDcZmVlhUKhwMTExG2jXAYGBshkMuzZs0fBT0REpE4pAMqGhYWFjVEuQRBsvG6M4eDBg2QyGXbu3BlihSIiIrIZFACF6elpcrkcV65cqXnddV2OHTvG6OgoO3bsCKk6ERER2WwNHwCttax6lpslj7mSz1zZp+gFeAH41mKAmGOIO4b2hMPOZIyelEt3yiXuNO4Wp7WWyclJcrkcN27cqFlLJpOcPHmS4eFhWlpaQqpQREREtkpDBsCVasCFZypMr3pMr3qseAGepWZQscGwHu8sYPnBmmMMcQe6kzH60i77W+Psb4/jNMCZtyAIuHjxIvl8nvn5+Zq11tbWjVEu8Xg8pApFRERkqxn73FRUx6y13Fj1OHOrzIXFCmV/7WO5xhAzBtcBB+54ccFai2/BsxYvWAuGxhg6Ew5D3UmOdiRpjTvb8Ik2V7Va3Rjlsry8XLPW2dlJJpPh8OHDGuUiIiLSBOo+AFYDy/nFCuPzJWaLPr61xIwh6ZpN69h5gaXkWyyWpGs4tCPBya4ku1uj3yUrlUobo1xKpVLN2q5du8hkMgwODupGr4iISBOp6wD49EqVR6+vMFfyAUi5DjFz5y7fy2WtpRxYqoHFdQzHOxO8Zleallj0OoJLS0ucOnWKiYkJPM+rWdu3bx/ZbJb+/n4FPxERkSZUlwGw4lu+N7NKYb6MF1jSMQd3G4OMtZZKsPanM+Fyz+40B3ZE4/Fn8/PzG6Ncas48GsPhw4fJZDJ0d3eHWKGIiIiEre4C4HO7fnHHkHRMaF2swFpWvADXGI53hdcNtNZy48YNcrkck5OTNWuxWGxjlEt7e/u21yYiIiLRUzcB0FrLk7MlnpwthtL1ezFlP9joBv7ivjZ2pbfncrW1litXrpDP55menq5ZSyaTDA8PMzQ0RCqV2pZ6REREpD7URQAMrOU7N1bJz5VxgZQbXtfvh1nvBqZjDq/f38beLbwg4vs+Fy5coFAosLCwULPW1tbGyMgIx48f1ygXEREReUGRD4CBtTx6fYWx+TIJx5B0o3fhYp21lmUvIOU63L+vjf3tmxvAqtUqZ86c4dSpU6ysrNSsdXV1kc1mOXToEI4T3e9IREREwhfpAGit5dtTq+TmSiQdh4Qbra7fC1kPgS0xhzfsb2PPJnQCi8UiY2NjnD59mnK5XLPW399PJpNh//79keuKioiISDRFOgA+MVPkiZkicUOkO3/Ptx4CW+MObxpsp7fl5Z0JfOaZZygUCpw9exbf92vWBgcHyWaz7Nq1azNKFhERkSYS2QB4dbnKw1eWwBLJOXt3Yq1lyQvY1RLjlw/ueEnPFZ6bmyOXy3Hp0qWaUS6O42yMcunq6tqKskVERKQJRPJZwGU/4LHrK3iBpa0Owx+szd1rdR1mih7fny1y1670i77fWsvU1BS5XI5r167VrMXjcY4fP87IyAhtbW1bWbaIiIg0gUgGwMeniyyUfVpjTl2fa3MdQyww/N/NEoPtCfpfYDyMtZbLly+Ty+WYnZ2tWUulUhujXJLJ5HaVLSIiIg0uclvA61u/hrVHu9W7H7YV7Ps+58+fJ5/Ps7i4WPMz7e3tjI6OcuzYMWKxSGZ0ERERqWORSheNsPX7fC+0FWyt5ZFHHmFqaqrmvT09PWQyGQ4ePKhRLiIiIrJlIhUAzyxUGmLr9/nWt4LzcyWGu1OkY6bmyR27d+8mm80yMDDQUJ9bREREoikyATCwlvH5EgaD04AhKOUalr2Ac4tlfmJnC/fffz9TU1McOHCAvr6+sMsTERGRJhKZM4CXlyo8fGWZpGOIvYSRKfVkuRrQmXR465GOyDzHWERERJpPZA6anVkoE1jbsOEP1rqAtyoBV5eqYZciIiIiTSwSAfBW2efKUpVEA4c/gJhjsNZyeqF85zeLiIiIbJFIBMBzixWqgW34AAiQcAyTy1Vulf07v1lERERkC0QiAF5fqWIwTXEDNuEYqoFluuiFXYqIiIg0qdADoB9YbpZ83MbPfgAbIXeupA6giIiIhCP0ALhQ8an4jX354/kMhhl1AEVERCQkoQfAuZKPby2xTc5/Zx7/Fr91spt3nOji4c99auP1wPf5xK/dxztOdPHBnx+huPwMXrXKl//6k3zovizvHO3jAz87xBcf/DClleXNLepZroGbJR8/iMQEHhEREWkykQiAwKaf/ztx1z3c97Z3A/Dlv3qIybNjADz8uU9xqfAkxhje+dBnaGnbwd995L185dMPcfP6JL0DB3hmfpZvfOEz/Pl7fp0gCDa1Lli7DVzxLQsVbQOLiIjI9gs9AM4WPQxbs/37lg98jD2HjuFVK3zu99/NpcL/8pW/+RMA7nv7ezj+6ru5PJ7nu1/7FwB+48MP8uAjT/Dev/g8AGe/9x2+/x8Pb3pdMQO+tToHKCIiIqEIPQCuepatOv4XT6Z41yc/ixuLMXl2nIfe/kb8apU9h47zlvd/FIBT3/rmxvtfed+bARh93f3EkykAxr79n5te13q3s+xrC1hERES2X+gB0Nvic3AHhrK88d0fBKBaLuG4Lu/65Gc3At78jac33rujpxcAx3Fo6+oGYG7q2pbUZTD40XgKn4iIiDSZ8AOgtVu0AfwD01cubvw98H1uPn31zj+05dnM4m3+8UIRERGROwo9AG7V+b91T37jq/zP174EQM+efQB8/uPvZ/HmDADd/Xs33vvM3CwAQRCwfGt+7Wd2D2xZbc0z+EZERESiJPQAGHO2rtm2eHOGz3/8AwCM/sx9/OEXv0FbZzfLC3P8w8feB8DIPfduvP/Jb34VgMKjX6daLgEwfPcvbEltFppq9qGIiIhER+gB0DVmywLg33/0d1lemKO1o4t3fOIv6ezr5+1/9GcA5P7r33nsX/+RA0NZ7nrgVwD4pz/+Az78wF18+n2/CcDRV7yGn7z3gS2qjqZ5+omIiIhES+gBsD3uEGzBZYhHv/QF8v/9dQDe9rE/pbOvH4BXvf6X+Kk3/SoAX3zoI9x8+irvfPAzvPl3PkTP7gFmJp+ivWsn977tt/m9v/1nHGfzv6L1z5uOhf71i4iISBMy1oZ7FfXx6VWemCnSHnfDLGNbVQOLZy1vPdxBZ7J5PreIiIhEQ+gtqJ2pGMCWdAGjygssSdfQkQj96xcREZEmFHoC6Um5xBxDM81E9qylLxXb9MffiYiIiPwoQg+AHQmHpGu2fCB01PS2aOtXREREwhF6ADTG0JeK4TXJFvD6Vvf61reIiIjIdgs9AALsa48DzXEOsOyvnf/bnVYAFBERkXBEIgAe6UiQdA3lBj8IaO3a7d/DHQnS8Uh89SIiItKEIpFC0jGHIx0JqtYS8lSaLeXZtcHXJzqTYZciIiIiTSwSARDgRFeKmDF4jZv/KPkBvS0u/dr+FRERkRBFJgDuanHpS8co+UHYpWyJ9fONQ90pjX8RERGRUEUmABpjGO5KYgwNORKm6Fla4w6HdsTDLkVERESaXGQCIKxdBtmdjrPqBw11FrAaWKyBV/e1kHQj9ZWLiIhIE4pUGnEdw+v2pEm5hmKD3Ai21lLyAwbb4gx16fKHiIiIhC9SARDWBiS/srcF39qG2Ape9SzpmMPP7Enr7J+IiIhEQuQCIECmJ8We1vrfCl7f+r1rVwsdCT36TURERKIhkgHwuVvBK359zgb0raWorV8RERGJoEgGQFjbCn7dnlZcQ92dBwysZcUL2Jly+bm9rdr6FRERkUiJbAAEONaZ5O7+NACrXn3MBwysZdkL6E66PDDYTpse+SYiIiIRE/l0MtKT4qf702BgxYv2mUD/2c5fV9LlDfvbde5PREREIsnYKCeq5xifL/HY1CpeYGmLOZHbVvUCy6q/tu37wKDCn4iIiERX3QRAgIuLFR6dWmG5GpByHRJO+CHQWsvqsxdVBtri3DvQpm1fERERibS6CoAASxWfb02tcmmpCkCra0LrBq53/Vpch1fvamGkO4kTsc6kiIiIyPPVXQCEta7bxK0K351eXesGOg5xh20LgoG1FJ/T9Xvdnla6ktryFRERkfpQlwFw3Xo38PJSFc9a4saQdM2WdeG8wFLyLRZLS8zhVX3q+omIiEj9qesACGvdwOmiz5mFEucXK5R9i8GQcg2xTTgjaK2lEqz9cYyhM+Ew1J3iaEeCtM76iYiISB2q+wD4XKtewPlbFcYXSiyUA4JnP5prDDFjiDm8aLfOWotvwbMWP4CAtZ+PO4bB9jgnupLsb4ur4yciIiJ1raEC4LrAWp5e8ZgpeswWPaaLPqtegB+A5YU/rsFgWevyxRzoTLj0p2P0pFwGWuN06oyfiIiINIiGDIDPZ599OsdcyWe+5FMJLF6wNrjZGIgZg2ugLe7Qk4rRnXRJuOryiYiISGNqigAoIiIiIj+gWwwiIiIiTUYBUERERKTJKACKiIiINBkFQBEREZEmowAoIiIi0mQUAEVERESajAKgiIiISJNRABQRERFpMgqAIiIiIk1GAVBERESkySgAioiIiDQZBUARERGRJqMAKCIiItJkFABFREREmowCoIiIiEiTUQAUERERaTIKgCIiIiJN5v8BVeEkoiTsDaEAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model = CausalModel(data=data['df'],\n",
    "                   treatment=data['treatment_name'],\n",
    "                   outcome=data['outcome_name'],\n",
    "                   graph=data['gml_graph'])\n",
    "\n",
    "model.view_model()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "951722fe-07e2-4ec3-8a2a-74a468fbd95e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Estimand type: EstimandType.NONPARAMETRIC_ATE\n",
      "\n",
      "### Estimand : 1\n",
      "Estimand name: backdoor\n",
      "Estimand expression:\n",
      "  d              \n",
      "─────(E[y|W0,W1])\n",
      "d[v₀]            \n",
      "Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W0,W1,U) = P(y|v0,W0,W1)\n",
      "\n",
      "### Estimand : 2\n",
      "Estimand name: iv\n",
      "Estimand expression:\n",
      " ⎡                              -1⎤\n",
      " ⎢    d        ⎛    d          ⎞  ⎥\n",
      "E⎢─────────(y)⋅⎜─────────([v₀])⎟  ⎥\n",
      " ⎣d[Z₀  Z₁]    ⎝d[Z₀  Z₁]      ⎠  ⎦\n",
      "Estimand assumption 1, As-if-random: If U→→y then ¬(U →→{Z0,Z1})\n",
      "Estimand assumption 2, Exclusion: If we remove {Z0,Z1}→{v0}, then ¬({Z0,Z1}→y)\n",
      "\n",
      "### Estimand : 3\n",
      "Estimand name: frontdoor\n",
      "No such variable(s) found!\n",
      "\n"
     ]
    }
   ],
   "source": [
    "identified_estimand= model.identify_effect(proceed_when_unidentifiable=True)\n",
    "\n",
    "print(identified_estimand)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "cb8ffe3c-0787-42a9-9c61-65d0c6d95831",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "8.218755524405902\n"
     ]
    }
   ],
   "source": [
    "causal_estimate = model.estimate_effect(\n",
    "    identified_estimand,\n",
    "    method_name=\"iv.instrumental_variable\")\n",
    "\n",
    "print(causal_estimate.value)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "fd0f1c68-b8c8-4553-87cc-7544c61a00ab",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7.4114527282113825\n"
     ]
    }
   ],
   "source": [
    "from sklearn.preprocessing import PolynomialFeatures\n",
    "from sklearn.linear_model import LassoCV\n",
    "from sklearn.ensemble import GradientBoostingRegressor\n",
    "\n",
    "dml_estimate = model.estimate_effect(\n",
    "    identified_estimand, \n",
    "    method_name=\"iv.econml.dml.DML\",\n",
    "    control_value = 0,\n",
    "    treatment_value = 1,\n",
    "    confidence_intervals=False,\n",
    "    method_params={\"init_params\":{'model_y':GradientBoostingRegressor(),\n",
    "                                  'model_t': GradientBoostingRegressor(),\n",
    "                                  \"model_final\":LassoCV(fit_intercept=False),\n",
    "                                  'featurizer':PolynomialFeatures(degree=1, include_bias=False)},\n",
    "                   \"fit_params\":{}})\n",
    "print(dml_estimate.value)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "88b683dd-ff48-42ee-a7ca-fe4e3c1f65d4",
   "metadata": {},
   "source": [
    "## Refute estimate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "4f098e86-2316-4088-8e52-12ce7d0fa08e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Refute: Add a random common cause\n",
      "Estimated effect:8.218755524405902\n",
      "New effect:8.218755524405902\n",
      "p value:1.0\n",
      "\n"
     ]
    }
   ],
   "source": [
    "res_random=model.refute_estimate(identified_estimand, causal_estimate, method_name=\"random_common_cause\")\n",
    "\n",
    "print(res_random)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3e798380-4be1-442c-abb9-c47feff06d09",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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