notebooks/Case_study1_The_Sars_cov2_model.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "cb1612e5198cab85",
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "source": [
    "# Case study 1: Simulation with a SARS-CoV-2 Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "efd9d834-3d8b-4044-806e-632920c8bd44",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-01-25T15:12:25.679553Z",
     "start_time": "2024-01-25T15:12:25.639493Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>key</th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>eliater</td>\n",
       "      <td>0.0.3-dev-28d9867e</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>y0</td>\n",
       "      <td>0.2.10-dev-8f27d998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Run at</td>\n",
       "      <td>2024-04-25 09:05:50</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       key                value\n",
       "0  eliater   0.0.3-dev-28d9867e\n",
       "1       y0  0.2.10-dev-8f27d998\n",
       "2   Run at  2024-04-25 09:05:50"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import Image, set_matplotlib_formats\n",
    "\n",
    "import eliater\n",
    "from eliater.examples.sars_cov2 import sars_large_example as example\n",
    "from y0.dsl import Variable\n",
    "\n",
    "set_matplotlib_formats(\"svg\")\n",
    "\n",
    "eliater.version_df()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "e3c3963b-36d3-468c-af44-2209e9ed24f8",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-01-25T15:12:25.716150Z",
     "start_time": "2024-01-25T15:12:25.677283Z"
    }
   },
   "outputs": [],
   "source": [
    "treatment = Variable(\"EGFR\")\n",
    "outcome = Variable(\"cytok\")\n",
    "SEED = 10"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5b60ed63441c5411",
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "source": [
    "This is case study 1 in Figure 6 in this paper: Eliater: an open source software for causal query estimation from observational measurements of biomolecular networks. The figure below shows the SARS-CoV-2 network (Mohammad-Taheri et al., 2022; Zucker\n",
    "et al., 2021), which models the activation of Cytokine Release Syndrome (Cytokine Storm), a known factor causing tissue damage in severely ill SARS-CoV-2 patients (Ulhaq and Soraya, 2020)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "180a282312bfb38f",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-01-25T16:23:52.389398600Z",
     "start_time": "2024-01-25T16:23:52.381627Z"
    },
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 3,
     "metadata": {
      "image/png": {
       "height": 500,
       "width": 500
      }
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Image(filename=\"../img/SARS_COV.png\", width=500, height=500)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "7ab5d32c79b7bf2",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-01-25T15:12:25.762655Z",
     "start_time": "2024-01-25T15:12:25.704336Z"
    },
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "graph = example.graph"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d5f92410e9801798",
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "source": [
    "This case study used synthetic observational data. The generation of this synthetic data was inspired by common biological practices.  The exogenous variables were modeled with a Gaussian distribution. For each endogenous variable $X$, we represented  biomolecular reactions using Hill equations \\cite{alon2019introduction}, and were approximated with a sigmoid function as follows,\n",
    "\n",
    "$\\mathcal{N}(\\frac{100}{1 + \\exp(\\mathbf{\\theta}^{\\prime} Pa(X) + \\theta_0)})$ \n",
    "\n",
    "where $Pa(X)$ is a $q \\times 1$ vector of measurements related to the parent of $X$, $\\mathbf{\\theta}'$ is a $1 \\times q$ parameter vector, and $\\theta_0$ is a scalar. The $EGFR$ was generated from a Binomial distribution with probability of $\\frac{1}{1 + \\exp(\\mathbf{\\theta}^{\\prime} Pa(X) + \\theta_0)}$. Hence, the observational data is mixed-type where the $EGFR$ column is binary (discrete), and rest of columns are continuous."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "eb3a23add3bc3914",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-01-25T15:12:25.858359Z",
     "start_time": "2024-01-25T15:12:25.719190Z"
    },
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
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       "    }\n",
       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SARS_COV2</th>\n",
       "      <th>ACE2</th>\n",
       "      <th>Ang</th>\n",
       "      <th>AGTR1</th>\n",
       "      <th>ADAM17</th>\n",
       "      <th>Toci</th>\n",
       "      <th>Sil6r</th>\n",
       "      <th>EGF</th>\n",
       "      <th>TNF</th>\n",
       "      <th>Gefi</th>\n",
       "      <th>EGFR</th>\n",
       "      <th>PRR</th>\n",
       "      <th>NFKB</th>\n",
       "      <th>IL6STAT3</th>\n",
       "      <th>IL6AMP</th>\n",
       "      <th>cytok</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>74.818466</td>\n",
       "      <td>18.273505</td>\n",
       "      <td>94.393956</td>\n",
       "      <td>98.801910</td>\n",
       "      <td>99.256863</td>\n",
       "      <td>36.651069</td>\n",
       "      <td>85.881712</td>\n",
       "      <td>94.749073</td>\n",
       "      <td>99.863592</td>\n",
       "      <td>33.966616</td>\n",
       "      <td>1</td>\n",
       "      <td>99.518860</td>\n",
       "      <td>34.272531</td>\n",
       "      <td>33.312144</td>\n",
       "      <td>43.843032</td>\n",
       "      <td>67.926041</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>50.907438</td>\n",
       "      <td>36.711375</td>\n",
       "      <td>67.699182</td>\n",
       "      <td>99.294325</td>\n",
       "      <td>96.954993</td>\n",
       "      <td>52.899809</td>\n",
       "      <td>75.568726</td>\n",
       "      <td>94.950873</td>\n",
       "      <td>98.934965</td>\n",
       "      <td>37.749754</td>\n",
       "      <td>1</td>\n",
       "      <td>88.868626</td>\n",
       "      <td>32.037571</td>\n",
       "      <td>71.296943</td>\n",
       "      <td>69.477027</td>\n",
       "      <td>91.129469</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>76.434215</td>\n",
       "      <td>17.909068</td>\n",
       "      <td>94.771813</td>\n",
       "      <td>100.384201</td>\n",
       "      <td>100.226176</td>\n",
       "      <td>42.669583</td>\n",
       "      <td>87.432246</td>\n",
       "      <td>95.490495</td>\n",
       "      <td>99.403036</td>\n",
       "      <td>37.181947</td>\n",
       "      <td>1</td>\n",
       "      <td>96.605436</td>\n",
       "      <td>35.853639</td>\n",
       "      <td>59.013137</td>\n",
       "      <td>61.840728</td>\n",
       "      <td>85.813060</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>72.508174</td>\n",
       "      <td>19.305827</td>\n",
       "      <td>93.568645</td>\n",
       "      <td>99.296364</td>\n",
       "      <td>99.259188</td>\n",
       "      <td>46.082585</td>\n",
       "      <td>71.540865</td>\n",
       "      <td>96.506473</td>\n",
       "      <td>99.042601</td>\n",
       "      <td>47.669759</td>\n",
       "      <td>1</td>\n",
       "      <td>96.954760</td>\n",
       "      <td>39.201726</td>\n",
       "      <td>20.074860</td>\n",
       "      <td>35.928344</td>\n",
       "      <td>56.811495</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>63.747777</td>\n",
       "      <td>26.183989</td>\n",
       "      <td>87.636441</td>\n",
       "      <td>98.737421</td>\n",
       "      <td>99.825670</td>\n",
       "      <td>55.442630</td>\n",
       "      <td>86.093502</td>\n",
       "      <td>96.710827</td>\n",
       "      <td>100.583294</td>\n",
       "      <td>42.514193</td>\n",
       "      <td>1</td>\n",
       "      <td>94.118504</td>\n",
       "      <td>29.089479</td>\n",
       "      <td>41.607462</td>\n",
       "      <td>45.669105</td>\n",
       "      <td>69.531180</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   SARS_COV2       ACE2        Ang       AGTR1      ADAM17       Toci  \\\n",
       "0  74.818466  18.273505  94.393956   98.801910   99.256863  36.651069   \n",
       "1  50.907438  36.711375  67.699182   99.294325   96.954993  52.899809   \n",
       "2  76.434215  17.909068  94.771813  100.384201  100.226176  42.669583   \n",
       "3  72.508174  19.305827  93.568645   99.296364   99.259188  46.082585   \n",
       "4  63.747777  26.183989  87.636441   98.737421   99.825670  55.442630   \n",
       "\n",
       "       Sil6r        EGF         TNF       Gefi  EGFR        PRR       NFKB  \\\n",
       "0  85.881712  94.749073   99.863592  33.966616     1  99.518860  34.272531   \n",
       "1  75.568726  94.950873   98.934965  37.749754     1  88.868626  32.037571   \n",
       "2  87.432246  95.490495   99.403036  37.181947     1  96.605436  35.853639   \n",
       "3  71.540865  96.506473   99.042601  47.669759     1  96.954760  39.201726   \n",
       "4  86.093502  96.710827  100.583294  42.514193     1  94.118504  29.089479   \n",
       "\n",
       "    IL6STAT3     IL6AMP      cytok  \n",
       "0  33.312144  43.843032  67.926041  \n",
       "1  71.296943  69.477027  91.129469  \n",
       "2  59.013137  61.840728  85.813060  \n",
       "3  20.074860  35.928344  56.811495  \n",
       "4  41.607462  45.669105  69.531180  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "data = example.generate_data(1000, seed=SEED)\n",
    "data.head()"
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  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "5802d94d9d86abda",
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     "start_time": "2024-01-25T15:12:25.840118Z"
    },
    "collapsed": false,
    "jupyter": {
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   "source": [
    "eliater.plot_treatment_and_outcome(data, treatment, outcome)"
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     "start_time": "2024-01-21T07:21:48.459711Z"
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   "source": [
    "## Step 1: Verify correctness of the network structure"
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    "We checked the consistency of the network structure against observational data with significance level of 0.01 by first discretizing the data into a binary data and used the $\\chi$-square test. This is because \\texttt{Eliater} does not support mixed-type data.  "
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    {
     "data": {
      "text/markdown": [
       "## Step 1: Checking the ADMG Structure"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
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     "data": {
      "text/markdown": [
       "On this try, we're going to discretize the data using K-Bins discretization with K as 2. Here are the first few rows of the transformed dataframe after doing that:"
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       "<IPython.core.display.Markdown object>"
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     "data": {
      "text/markdown": [
       "Of the 99 d-separations implied by the ADMG's structure, only 9 (9.09%) rejected the null hypothesis for the cressie_read test at p<0.01.\n",
       "\n",
       "Since this is less than 30%, Eliater considers this minor and leaves the ADMG unmodified. Finished in 7.62 seconds.\n"
      ],
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       "<IPython.core.display.Markdown object>"
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     "data": {
      "text/markdown": [
       "| left     | right     | given     |    stats |           p |   dof |       p_adj | p_adj_significant   |\n",
       "|:---------|:----------|:----------|---------:|------------:|------:|------------:|:--------------------|\n",
       "| ADAM17   | PRR       | SARS_COV2 |  76.1544 | 0           |     1 | 0           | True                |\n",
       "| AGTR1    | PRR       | SARS_COV2 | 108.975  | 0           |     1 | 0           | True                |\n",
       "| Ang      | PRR       | SARS_COV2 | 126.901  | 0           |     1 | 0           | True                |\n",
       "| ACE2     | PRR       | SARS_COV2 |  63.0893 | 1.9984e-15  |     1 | 1.91847e-13 | True                |\n",
       "| Sil6r    | cytok     | IL6AMP    |  58.8112 | 1.73195e-14 |     1 | 1.64535e-12 | True                |\n",
       "| IL6STAT3 | cytok     | IL6AMP    |  42.1103 | 8.62689e-11 |     1 | 8.10927e-09 | True                |\n",
       "| NFKB     | SARS_COV2 | AGTR1;PRR |  29.5691 | 5.39582e-08 |     1 | 5.01811e-06 | True                |\n",
       "| IL6STAT3 | Toci      | Sil6r     |  30.6843 | 2.1726e-07  |     2 | 1.99879e-05 | True                |\n",
       "| Toci     | cytok     | IL6AMP    |  18.1308 | 2.0624e-05  |     1 | 0.00187679  | True                |"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "eliater.step_1_notebook(graph=graph, data=data, binarize=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "74b79e259232adfb",
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   "source": [
    "**Note** As the data was synthetically generated based on the network structure, we expected all the tests to pass. However, these failures might have been introduced by the discretization process."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "5874af0e-e8de-4f64-86e0-1f7d01545941",
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    {
     "data": {
      "text/markdown": [
       "\n",
       "## Step 2: Check Query Identifiability\n",
       "\n",
       "The causal query of interest is the average treatment effect of $EGFR$ on $cytok$, defined as:\n",
       "$\\mathbb{E}[cytok \\mid do(EGFR=1)] - \\mathbb{E}[cytok \\mid do(EGFR=0)]$.\n",
       "\n",
       "\n",
       "Running the ID algorithm defined by [Identification of joint interventional distributions in recursive\n",
       "semi-Markovian causal models](https://dl.acm.org/doi/10.5555/1597348.1597382) (Shpitser and Pearl, 2006)\n",
       "and implemented in the $Y_0$ Causal Reasoning Engine gives the following estimand:\n",
       "\n",
       "$\\sum\\limits_{ACE_2, ADAM_{17}, AGTR_1, Ang, IL6AM_P, IL6STA_{T3}, NFKB, PRR, SARS_COV_2, Sil6_r, TNF, Toci} P(ACE_2 | SARS_COV_2) P(AGTR_1 | ACE_2, Ang, SARS_COV_2) P(IL6AM_P | ACE_2, ADAM_{17}, AGTR_1, Ang, EGF, EGFR, Gefi, IL6STA_{T3}, NFKB, PRR, SARS_COV_2, Sil6_r, TNF, Toci) P(IL6STA_{T3} | ACE_2, ADAM_{17}, AGTR_1, Ang, SARS_COV_2, Sil6_r, Toci) P(TNF | ACE_2, ADAM_{17}, AGTR_1, Ang, SARS_COV_2) P(cytok | ACE_2, ADAM_{17}, AGTR_1, Ang, EGF, EGFR, Gefi, IL6AM_P, IL6STA_{T3}, NFKB, PRR, SARS_COV_2, Sil6_r, TNF, Toci) \\sum\\limits_{ACE_2, ADAM_{17}, AGTR_1, Ang, EGF, EGFR, Gefi, IL6AM_P, IL6STA_{T3}, NFKB, PRR, SARS_COV_2, Sil6_r, TNF, cytok} P(ACE_2, ADAM_{17}, AGTR_1, Ang, EGF, EGFR, Gefi, IL6AM_P, IL6STA_{T3}, NFKB, PRR, SARS_COV_2, Sil6_r, TNF, Toci, cytok) P(ADAM_{17} | ACE_2, AGTR_1, Ang, SARS_COV_2, Toci) P(Sil6_r | ACE_2, ADAM_{17}, AGTR_1, Ang, SARS_COV_2, Toci) P(Ang | ACE_2, SARS_COV_2) P(SARS_COV_2) P(NFKB | ACE_2, ADAM_{17}, AGTR_1, Ang, EGF, EGFR, Gefi, PRR, SARS_COV_2, TNF) P(PRR | ACE_2, Gefi, SARS_COV_2)$\n",
       "\n",
       "Because the query is identifiable, we can proceed to Step 3.\n"
      ],
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       "<IPython.core.display.Markdown object>"
      ]
     },
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    }
   ],
   "source": [
    "eliater.step_2_notebook(graph=graph, treatment=treatment, outcome=outcome)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "4dff38fbf23ce61c",
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    {
     "data": {
      "text/markdown": [
       "## Step 3/4: Identify Nuisance Variables and Simplify the ADMG"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
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    {
     "data": {
      "text/markdown": [
       "No variables were identified as nuisance variables.\n",
       "\n",
       "Nevertheless, the algorithm proposed in [Graphs for margins of Bayesian\n",
       "networks](https://arxiv.org/abs/1408.1809) (Evans, 2016) and implemented in\n",
       "the $Y_0$ Causal Reasoning Engine is applied to the ADMG to attempt to\n",
       "simplify the graph by reasoning over its bidirected edges (if they exist).\n"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
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    },
    {
     "data": {
      "text/markdown": [
       "The simplification did not modify the graph."
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reduced_graph = eliater.step_3_notebook(graph=graph, treatment=treatment, outcome=outcome)\n",
    "reduced_graph is not None"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bcdeff27d0bcbcb3",
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   "source": [
    "## Evaluation criterion\n",
    "As we used synthetic data set, we were able to generate two interventional data sets where in\n",
    "one EGFR was set to 1, and the other one EGFR is set to 0. The ATE was calculated by subtracting the average value of Cytokine Storm obtained from each interventional data,\n",
    "resulting in the ground truth ATE=0.796. The positive ATE indicates that the Gefitinib drug can not reduce the Cytokine Storm levels, hence can't help in treating patients having SARS-Cov-2."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "14bdcdd244526750",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-01-25T15:13:17.847234Z",
     "start_time": "2024-01-25T15:13:17.729510Z"
    },
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    {
     "data": {
      "text/markdown": [
       "## Step 5: Estimate the Query\n",
       "\n",
       "### Calculating the True Average Treatment Effect (ATE)\n",
       "\n",
       "We first generated synthetic observational data. Now, we generate two interventional datasets:\n",
       "one where we set $EGFR$ to $0.0$ and one where we set $EGFR$ to $1.0$.\n",
       "We can then calculate the \"true\" average treatment effect (ATE) as the difference of the means\n",
       "for the outcome variable $cytok$ in each. The ATE is formulated as:\n",
       "\n",
       "$ATE = \\mathbb{E}[cytok \\mid do(EGFR = 1)] - \\mathbb{E}[cytok \\mid do(EGFR = 0)]$\n",
       "\n",
       "After generating 10,000 samples for each distribution, we took 500 subsamples of size\n",
       "of size 1,000 and calculated the\n",
       "ATE for each. The variance comes to 1.8e-05, which shows that the ATE is very stable with respect\n",
       "to random generation. We therefore calculate the _true_ ATE as the average value from these samplings,\n",
       "which comes to 7.9e-01.\n",
       "\n",
       "The ATE can be interpreted in the following way:\n",
       "\n",
       "1. If the ATE is positive, it suggests that the treatment $EGFR$ has a negative effect on the outcome $cytok$\n",
       "2. If the ATE is negative, it suggests that the treatment $EGFR$ has a positive effect on the outcome $cytok$\n",
       "\n",
       "### Estimating the Average Treatment Effect (ATE)\n",
       "\n",
       "In practice, we are often unable to get the appropriate interventional data, and therefore want to estimate\n",
       "the average treatment effect (ATE) from observational data. Because we're using synthetic data, we generate\n",
       "10,000 samples, then took 500 subsamples of size 1,000 through which we calculated\n",
       "the following:\n",
       "\n",
       "1. The ATE, using the y0/ananke implementation\n",
       "2. The ATE, using the Eliater linear regression implementation\n"
      ],
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       "<IPython.core.display.Markdown object>"
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      "text/plain": [
       "Subsampling:   0%|          | 0/500 [00:00<?, ?sample/s]"
      ]
     },
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      },
      "text/plain": [
       "Estimating:   0%|          | 0/500 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "eliater.step_5_notebook_synthetic(\n",
    "    graph=graph,\n",
    "    reduced_graph=reduced_graph,\n",
    "    example=example,\n",
    "    treatment=treatment,\n",
    "    outcome=outcome,\n",
    "    seed=SEED,\n",
    ")"
   ]
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