Lecture 06 - Good & Bad Controls.ipynb

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   "source": [
    "# [Lecture 06 - Good & Bad Controls](https://youtu.be/uanZZLlzKHw?si=ODJXflnZndT_SMe9)"
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      "\u001b[31mWatermark:\n",
      "----------\n",
      "\u001b[34mLast updated: 2023-08-29T13:07:54.450759-07:00\n",
      "\n",
      "Python implementation: CPython\n",
      "Python version       : 3.10.12\n",
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    "%run init_notebook.py"
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    "# Avoid being clever at all costs\n",
    "Being clever is\n",
    "- unreliable\n",
    "- opaque\n",
    "\n",
    "Using explicit causal models allows one to:\n",
    "- derive implications using logic\n",
    "- verify work & assumptions\n",
    "- facilitates peer review & verification"
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    "# Confounds\n",
    "## Review\n",
    "- The Fork $X \\leftarrow Z \\rightarrow Y$\n",
    "  - $X$ adn $Y$ are associated unless we stratify by $Z$\n",
    "- The Pipe $X \\rightarrow Z \\rightarrow Y$\n",
    "  - $X$ adn $Y$ are associated unless we stratify by $Z$\n",
    "- The Fork $X \\rightarrow Z \\leftarrow Y$\n",
    "  - $X$ adn $Y$ are not associated unless we stratify by $Z$\n",
    "- The Descendant $Z \\rightarrow A$\n",
    "  - Descendent $A$ takes on behavior of parent $Z$\n"
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    ")"
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    "utils.draw_causal_graph(\n",
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    "        (\"U\", \"Y\"),\n",
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    "The gold standard is **Randomization**. However randomization often this generally isn't possible:\n",
    "- impossible\n",
    "- pragmatism\n",
    "- ethical concerns\n",
    "- unmeasured confounds\n",
    "\n",
    "## Causal Thinking\n",
    "- We would like a procedure $do(X)$ that intervenes on $X$ in such a way that it can \"mimic\" the effect of randomization.\n",
    "- Such a procedure would transform the Confounded graph:\n",
    "\n",
    "#### Without randomization"
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    "utils.draw_causal_graph(\n",
    "    [\n",
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    "in such a way that all the non-causal arrows entering X have been removed"
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   "source": [
    "#### With \"randomization\" induced by $do(X)$"
   ]
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       "<text text-anchor=\"middle\" x=\"27\" y=\"-12.95\" font-family=\"Times,serif\" font-size=\"14.00\">X</text>\n",
       "</g>\n",
       "<!-- X&#45;&gt;Y -->\n",
       "<g id=\"edge2\" class=\"edge\">\n",
       "<title>X&#45;&gt;Y</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M53.76,-22C71.25,-24.86 94.85,-28.98 115.5,-33.5 118.54,-34.16 121.68,-34.9 124.83,-35.66\"/>\n",
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       "<text text-anchor=\"middle\" x=\"93.75\" y=\"-37.2\" font-family=\"Times,serif\" font-size=\"14.00\">do(X) &#160;&#160;</text>\n",
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       "</g>\n",
       "</svg>\n"
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     },
     "execution_count": 5,
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   ],
   "source": [
    "utils.draw_causal_graph(\n",
    "    [\n",
    "        (\"U\", \"Y\"),\n",
    "        (\"X\", \"Y\"),\n",
    "    ],\n",
    "    edge_props={\n",
    "        (\"X\", \"Y\"): {\"label\": \"do(X)   \"}\n",
    "    },\n",
    "    graph_direction=\"LR\"\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eb7fc683-405d-4af1-ab8b-9899b0ee46cf",
   "metadata": {},
   "source": [
    "It turns out that we can analyze graph structure to determine if there is such a procedure that exists"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "098982b1",
   "metadata": {},
   "source": [
    "## Example: Simple Confound"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "3cb4d99a",
   "metadata": {},
   "outputs": [
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   "source": [
    "utils.draw_causal_graph(\n",
    "    edge_list=[\n",
    "        (\"U\", \"Y\"),\n",
    "        (\"U\", \"X\"),\n",
    "        (\"X\", \"Y\")\n",
    "    ],\n",
    "    node_props={\n",
    "        \"U\": {\"color\": \"red\"},\n",
    "    },\n",
    "    edge_props={\n",
    "        (\"U\", \"X\"): {\"color\": \"red\"},\n",
    "        (\"U\", \"Y\"): {\"color\": \"red\"}\n",
    "    },\n",
    "    graph_direction=\"LR\"\n",
    ")"
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  {
   "cell_type": "markdown",
   "id": "4d5ee8fc",
   "metadata": {},
   "source": [
    "In the Fork example, we've shown that stratifying by the confound, we \"close\" the fork by conditioning on U, thus removing any of the causal effect of U on X, thus allowing us to isolate the treatment's effect on Y.\n",
    "\n",
    "This procedure is part of what is known as Do-calculus. The operator `do(X)` tends to mean intervening on X (i.e. setting it to a specific value that is independent of the confound)\n",
    "\n",
    "$$\n",
    "p(Y | do(X)) = \\sum_U p(Y | X, U)p(U) = \\mathbb{E}_U[p(Y | X, U)]\n",
    "$$\n",
    "\n",
    "i.e. **the procedure that gives us the intervention on X is equivalent of the distribution of Y, stratified by the treatment X and the confound X, averaged over the distribution of the confound.**\n",
    "\n",
    "> Note that when we use linear regression estimator for each X, we are **implicity marginalizing and averaging** over out treatment and confound (e.g. in the model form $Y \\sim \\mathcal{N}(\\alpha + \\beta_X X + \\beta_Z Z, \\sigma^2)$\n",
    "\n",
    "- it is generally **not the estimated coefficient** in the model that relate X to Y\n",
    "- it is the distribution of Y when we change X, **averaged over the distribution** defined by the control/confound variables (i.e. U)\n",
    "\n",
    "## Do-calculus\n",
    "- Applied to DAGs, provides a set of rules for identifying $p(Y | do(X))$\n",
    "- **Informs what is possible** before picking functions or distributions\n",
    "- **Justifies graphical analysis**\n",
    "- If do-calculus claims that inference is possible no further special assumptions are required for inference\n",
    "  - often additional assumptions can make the inference even stronger\n",
    "\n",
    "## Backdoor Criterion\n",
    "**Shortcut for applying Do-calculus graphically with your eyeballs.** General rule for finding the minimal sufficient adjustment set of variables to condition on.\n",
    "\n",
    "1. Identify **all** paths connecting treatment X to to outcome Y, including those entering and exiting X (association can be directe/undirected, causation is directed)\n",
    "2. Any of those paths **entering X** are backdoor (non-causal) paths\n",
    "3. Find the **adjustment set** of variables that, once conditioned on, \"closes/blocks\" all the backdoor paths identified in\n",
    "\n",
    "#### Backdoor Criterion Example"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "f469310b",
   "metadata": {
    "jupyter": {
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   "outputs": [
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   ],
   "source": [
    "utils.draw_causal_graph(\n",
    "    [\n",
    "        (\"U\", \"Z\"),\n",
    "        (\"Z\", \"X\"),\n",
    "        (\"U\", \"Y\"),\n",
    "        (\"X\", \"Y\")\n",
    "    ],\n",
    "    node_props={\n",
    "        \"Z\": {\"color\": \"red\"},\n",
    "        \"U\": {\"style\": \"dashed\"}\n",
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    "        (\"U\", \"Z\"): {\"color\": \"red\"},\n",
    "        (\"Z\", \"X\"): {\"color\": \"red\"},\n",
    "        (\"U\", \"Y\"): {\"color\": \"red\"}\n",
    "    },\n",
    "    graph_direction=\"LR\"\n",
    "\n",
    ")"
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   "cell_type": "markdown",
   "id": "b563ea62",
   "metadata": {},
   "source": [
    "Backdoor path highlighted in red.\n",
    "\n",
    "- If we could measure $U$ we could just stratify by $U$; however, it is unobserved.\n",
    "- However, we can block the backdoor path by conditioning on $Z$, despite not being able to measure $U$.\n",
    "- This works because $Z$ \"knows\" everything we need to know about association between $X$, $Y$ that is due to the unmeasured confound $U$.\n",
    "\n",
    "#### Resulting graph after stratifying by $Z$\n",
    "- The $U \\rightarrow Z \\rightarrow X$ Pipe has now been broken, disassociateing $X$ from the confound $U$\n",
    "- Note: this doesn't remove the confound's effect on $Y$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "bc2024ac",
   "metadata": {
    "jupyter": {
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   },
   "outputs": [
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   "source": [
    "utils.draw_causal_graph(\n",
    "    [\n",
    "        (\"U\", \"Z\"),\n",
    "        (\"Z\", \"X\"),\n",
    "        (\"U\", \"Y\"),\n",
    "        (\"X\", \"Y\")\n",
    "    ],\n",
    "    node_props={\n",
    "        \"Z\": {\"color\": \"red\"},\n",
    "        \"U\": {\"style\": \"dashed\"}\n",
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    "    edge_props={\n",
    "        (\"U\", \"Z\"): {\"color\": \"red\"},\n",
    "        (\"Z\", \"X\"): {\"color\": \"none\"},\n",
    "        (\"U\", \"Y\"): {\"color\": \"red\"}\n",
    "    },\n",
    "    graph_direction=\"LR\"\n",
    "\n",
    ")"
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  {
   "cell_type": "markdown",
   "id": "bae86319",
   "metadata": {},
   "source": [
    "### Validate through simulation\n",
    "\n",
    "Here we simulate a situation where Y is caused by X and and an unmeasured confound U that also effects Z and X. (We could also prove mathematically, but simulation is quite confincing as well--for me anyways)\n",
    "\n",
    "$$\n",
    "\\begin{align*}\n",
    "U &\\sim \\text{Bernoulli}(0.5) \\\\\n",
    "Z &\\sim \\text{Normal}(\\beta_{UZ}U, 1) \\\\\n",
    "X &\\sim \\text{Normal}(\\beta_{ZX}Z, 1) \\\\\n",
    "Y &\\sim \\text{Normal}(\\alpha + \\beta_{XY}X + \\beta_{UY}U, 1) \\\\\n",
    "\\end{align*}\n",
    "$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "d17b2ecf",
   "metadata": {},
   "outputs": [],
   "source": [
    "np.random.seed(123)\n",
    "n_samples = 200\n",
    "\n",
    "alpha = 0\n",
    "beta_XY = 0\n",
    "beta_UY = -1\n",
    "beta_UZ = -1\n",
    "beta_ZX = 1\n",
    "\n",
    "U = stats.bernoulli.rvs(p=.5, size=n_samples)\n",
    "Z = stats.norm.rvs(loc=beta_UZ * U)\n",
    "X = stats.norm.rvs(loc=beta_ZX * Z)\n",
    "Y = stats.norm.rvs(loc=alpha + beta_XY * X + beta_UY * U)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5e9652b3",
   "metadata": {},
   "source": [
    "### Unstratified (confounded) Model\n",
    "\n",
    "\n",
    "$$\n",
    "\\begin{align*}\n",
    "Y &\\sim \\text{Normal}(\\mu_Y, \\sigma_Y) \\\\\n",
    "\\mu_Y &= \\alpha + \\beta_{XY}X \\\\\n",
    "\\alpha &\\sim \\text{Normal}(0, 1) \\\\\n",
    "\\beta_{XY} &\\sim \\text{Normal}(0, 1) \\\\\n",
    "\\sigma_Y &\\sim \\text{Exponential}(1)\n",
    "\\end{align*}\n",
    "$$"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6b78675d-a08e-4a0e-a6a5-fdd48c8212e4",
   "metadata": {},
   "source": [
    "#### Fit the unstratified model, ignoring Z (and U)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "bb2785d8",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Auto-assigning NUTS sampler...\n",
      "Initializing NUTS using jitter+adapt_diag...\n",
      "Multiprocess sampling (4 chains in 4 jobs)\n",
      "NUTS: [alpha, beta_XY, sigma]\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "<style>\n",
       "    /* Turns off some styling */\n",
       "    progress {\n",
       "        /* gets rid of default border in Firefox and Opera. */\n",
       "        border: none;\n",
       "        /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
       "        background-size: auto;\n",
       "    }\n",
       "    progress:not([value]), progress:not([value])::-webkit-progress-bar {\n",
       "        background: repeating-linear-gradient(45deg, #7e7e7e, #7e7e7e 10px, #5c5c5c 10px, #5c5c5c 20px);\n",
       "    }\n",
       "    .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
       "        background: #F44336;\n",
       "    }\n",
       "</style>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "\n",
       "    <div>\n",
       "      <progress value='8000' class='' max='8000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
       "      100.00% [8000/8000 00:00&lt;00:00 Sampling 4 chains, 0 divergences]\n",
       "    </div>\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Sampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 1 seconds.\n"
     ]
    }
   ],
   "source": [
    "with pm.Model() as unstratified_model:\n",
    "    # Priors\n",
    "    alpha_ = pm.Normal(\"alpha\", 0, 1)\n",
    "    beta_XY_ = pm.Normal(\"beta_XY\", 0, 1)\n",
    "    sigma_ = pm.Exponential(\"sigma\", 1)\n",
    "    \n",
    "    # Likelihood\n",
    "    mu_ = alpha_ + beta_XY_ * X\n",
    "    Y_ = pm.Normal(\"Y\", mu=mu_, sigma=sigma_, observed=Y)\n",
    "    unstratified_model_inference = pm.sample()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "b4cfb112",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>sd</th>\n",
       "      <th>hdi_3%</th>\n",
       "      <th>hdi_97%</th>\n",
       "      <th>mcse_mean</th>\n",
       "      <th>mcse_sd</th>\n",
       "      <th>ess_bulk</th>\n",
       "      <th>ess_tail</th>\n",
       "      <th>r_hat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>alpha</th>\n",
       "      <td>-0.380</td>\n",
       "      <td>0.087</td>\n",
       "      <td>-0.543</td>\n",
       "      <td>-0.216</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.001</td>\n",
       "      <td>4060.0</td>\n",
       "      <td>3333.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>beta_XY</th>\n",
       "      <td>0.149</td>\n",
       "      <td>0.056</td>\n",
       "      <td>0.050</td>\n",
       "      <td>0.257</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.001</td>\n",
       "      <td>3734.0</td>\n",
       "      <td>3062.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sigma</th>\n",
       "      <td>1.114</td>\n",
       "      <td>0.056</td>\n",
       "      <td>1.008</td>\n",
       "      <td>1.215</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.001</td>\n",
       "      <td>5373.0</td>\n",
       "      <td>3124.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          mean     sd  hdi_3%  hdi_97%  mcse_mean  mcse_sd  ess_bulk  \\\n",
       "alpha   -0.380  0.087  -0.543   -0.216      0.001    0.001    4060.0   \n",
       "beta_XY  0.149  0.056   0.050    0.257      0.001    0.001    3734.0   \n",
       "sigma    1.114  0.056   1.008    1.215      0.001    0.001    5373.0   \n",
       "\n",
       "         ess_tail  r_hat  \n",
       "alpha      3333.0    1.0  \n",
       "beta_XY    3062.0    1.0  \n",
       "sigma      3124.0    1.0  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "az.summary(unstratified_model_inference)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3f742bec",
   "metadata": {},
   "source": [
    "### Stratifying by Z (unconfounded)\n",
    "\n",
    "\n",
    "$$\n",
    "\\begin{align*}\n",
    "Y &\\sim \\text{Normal}(\\mu_Y, \\sigma_Y) \\\\\n",
    "\\mu_Y = &\\alpha + \\beta_{XY}X + \\beta_{Z}Z \\\\\n",
    "\\alpha &\\sim \\text{Normal}(0, 1) \\\\\n",
    "\\beta_{*} &\\sim \\text{Normal}(0, 1) \\\\\n",
    "\\sigma_Y &\\sim \\text{Exponential}(1)\n",
    "\\end{align*}\n",
    "$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "74eabb3d",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Auto-assigning NUTS sampler...\n",
      "Initializing NUTS using jitter+adapt_diag...\n",
      "Multiprocess sampling (4 chains in 4 jobs)\n",
      "NUTS: [alpha, beta_XY, beta_Z, sigma]\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "<style>\n",
       "    /* Turns off some styling */\n",
       "    progress {\n",
       "        /* gets rid of default border in Firefox and Opera. */\n",
       "        border: none;\n",
       "        /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
       "        background-size: auto;\n",
       "    }\n",
       "    progress:not([value]), progress:not([value])::-webkit-progress-bar {\n",
       "        background: repeating-linear-gradient(45deg, #7e7e7e, #7e7e7e 10px, #5c5c5c 10px, #5c5c5c 20px);\n",
       "    }\n",
       "    .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
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       "</style>\n"
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     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "\n",
       "    <div>\n",
       "      <progress value='8000' class='' max='8000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
       "      100.00% [8000/8000 00:01&lt;00:00 Sampling 4 chains, 0 divergences]\n",
       "    </div>\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Sampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 1 seconds.\n"
     ]
    }
   ],
   "source": [
    "# Fit the stratified Model\n",
    "with pm.Model() as stratified_model:\n",
    "    # Priors\n",
    "    alpha_ = pm.Normal(\"alpha\", 0, 1)\n",
    "    beta_XY_ = pm.Normal(\"beta_XY\", 0, 1)\n",
    "    beta_Z_ = pm.Normal(\"beta_Z\", 0, 1)\n",
    "    sigma_ = pm.Exponential(\"sigma\", 1)\n",
    "    \n",
    "    # Likelihood (includes Z)\n",
    "    mu_ = alpha_ + beta_XY_ * X + beta_Z_ * Z\n",
    "    Y_ = pm.Normal(\"Y\", mu=mu_, sigma=sigma_, observed=Y)\n",
    "    stratified_model_inference = pm.sample()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "5f3f2726",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>sd</th>\n",
       "      <th>hdi_3%</th>\n",
       "      <th>hdi_97%</th>\n",
       "      <th>mcse_mean</th>\n",
       "      <th>mcse_sd</th>\n",
       "      <th>ess_bulk</th>\n",
       "      <th>ess_tail</th>\n",
       "      <th>r_hat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>alpha</th>\n",
       "      <td>-0.285</td>\n",
       "      <td>0.088</td>\n",
       "      <td>-0.445</td>\n",
       "      <td>-0.117</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.001</td>\n",
       "      <td>3813.0</td>\n",
       "      <td>2990.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>beta_XY</th>\n",
       "      <td>-0.026</td>\n",
       "      <td>0.081</td>\n",
       "      <td>-0.178</td>\n",
       "      <td>0.126</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.001</td>\n",
       "      <td>2914.0</td>\n",
       "      <td>2717.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>beta_Z</th>\n",
       "      <td>0.319</td>\n",
       "      <td>0.108</td>\n",
       "      <td>0.109</td>\n",
       "      <td>0.516</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.001</td>\n",
       "      <td>2851.0</td>\n",
       "      <td>2689.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sigma</th>\n",
       "      <td>1.090</td>\n",
       "      <td>0.055</td>\n",
       "      <td>0.987</td>\n",
       "      <td>1.193</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.001</td>\n",
       "      <td>3816.0</td>\n",
       "      <td>2734.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          mean     sd  hdi_3%  hdi_97%  mcse_mean  mcse_sd  ess_bulk  \\\n",
       "alpha   -0.285  0.088  -0.445   -0.117      0.001    0.001    3813.0   \n",
       "beta_XY -0.026  0.081  -0.178    0.126      0.002    0.001    2914.0   \n",
       "beta_Z   0.319  0.108   0.109    0.516      0.002    0.001    2851.0   \n",
       "sigma    1.090  0.055   0.987    1.193      0.001    0.001    3816.0   \n",
       "\n",
       "         ess_tail  r_hat  \n",
       "alpha      2990.0    1.0  \n",
       "beta_XY    2717.0    1.0  \n",
       "beta_Z     2689.0    1.0  \n",
       "sigma      2734.0    1.0  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "az.summary(stratified_model_inference)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5155001e",
   "metadata": {},
   "source": [
    "**NOTE**: the model coefficient `beta_Z` means nothing in in terms of causal effect of $Z$ on $Y$. In order to determine the causal effect of $Z$ on $Y$ you'd need a different estimator. In general, variables in the adjustment set are not interpretable. This is related to the \"Table 2 Fallacy\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "918a23d4-e27a-4411-92dd-3274bca22efd",
   "metadata": {},
   "source": [
    "#### Compare stratified and unstratified models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "a5fa519a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 800x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "_, ax = plt.subplots(figsize=(8, 3))\n",
    "az.plot_dist(unstratified_model_inference.posterior.beta_XY, color='k', label='Y|X', ax=ax)\n",
    "az.plot_dist(stratified_model_inference.posterior.beta_XY, color='C0', label='Y|X,Z', ax=ax)\n",
    "plt.axvline(beta_XY, color='C0', linestyle='--', alpha=.5, label=f\"Actual={beta_XY}\")\n",
    "plt.xlim([-.5, .5])\n",
    "plt.xlabel(\"posterior beta_XY\")\n",
    "plt.ylabel(\"density\")\n",
    "plt.legend();"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "036b64b3-5fe8-4d39-95b5-3beb8e92fec9",
   "metadata": {},
   "source": [
    "## More Complicated Example"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "2fc8aca5-c156-414e-ac58-8b7ca9b4c066",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<graphviz.graphs.Digraph at 0x11e6e77f0>"
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     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "utils.draw_causal_graph(\n",
    "    edge_list=[\n",
    "        (\"A\", \"Z\"),\n",
    "        (\"A\", \"X\"),\n",
    "        (\"Z\", \"X\"),\n",
    "        (\"B\", \"Z\"),\n",
    "        (\"B\", \"Y\"),\n",
    "        (\"Z\", \"Y\"),\n",
    "        (\"X\", \"Y\"),\n",
    "        (\"C\", \"X\"),\n",
    "        (\"C\", \"Y\")\n",
    "    ],\n",
    "    graph_direction=\"LR\"\n",
    ")\n"
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  {
   "cell_type": "markdown",
   "id": "30a9a524-a318-412c-bfd6-2247103d6da6",
   "metadata": {},
   "source": [
    "### All Paths Connecting X to Y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "7b38d191-7a84-4629-9c89-158ede09ad3f",
   "metadata": {},
   "outputs": [
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     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "utils.draw_causal_graph(\n",
    "    edge_list=[\n",
    "        (\"A\", \"Z\"),\n",
    "        (\"A\", \"X\"),\n",
    "        (\"Z\", \"X\"),\n",
    "        (\"B\", \"Z\"),\n",
    "        (\"B\", \"Y\"),\n",
    "        (\"Z\", \"Y\"),\n",
    "        (\"X\", \"Y\"),\n",
    "        (\"C\", \"X\"),\n",
    "        (\"C\", \"Y\")\n",
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  {
   "cell_type": "markdown",
   "id": "873fe316-98ea-41bf-b97b-44759afe8ea3",
   "metadata": {},
   "source": [
    "#### $X \\rightarrow Y$\n",
    "- Direct, causal path, leave open"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "7d6f562e-f610-40e6-99cf-b812c2ce86c2",
   "metadata": {},
   "outputs": [
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     },
     "execution_count": 17,
     "metadata": {},
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   ],
   "source": [
    "utils.draw_causal_graph(\n",
    "    edge_list=[\n",
    "        (\"A\", \"Z\"),\n",
    "        (\"A\", \"X\"),\n",
    "        (\"Z\", \"X\"),\n",
    "        (\"B\", \"Z\"),\n",
    "        (\"B\", \"Y\"),\n",
    "        (\"Z\", \"Y\"),\n",
    "        (\"X\", \"Y\"),\n",
    "        (\"C\", \"X\"),\n",
    "        (\"C\", \"Y\")\n",
    "    ],\n",
    "    edge_props={\n",
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    "    graph_direction=\"LR\"\n",
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  {
   "cell_type": "markdown",
   "id": "9d9e70ce-0043-42e0-8337-ffe28f9497f0",
   "metadata": {},
   "source": [
    "#### $X \\leftarrow C \\rightarrow Y$\n",
    "- Backdoor non-causal path\n",
    "- Block by **stratifying by $C$**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "410970d8-34e7-4852-b2df-c58d062da5d8",
   "metadata": {},
   "outputs": [
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       "<!-- B&#45;&gt;Z -->\n",
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       "<title>B&#45;&gt;Z</title>\n",
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     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "utils.draw_causal_graph(\n",
    "    edge_list=[\n",
    "        (\"A\", \"Z\"),\n",
    "        (\"A\", \"X\"),\n",
    "        (\"Z\", \"X\"),\n",
    "        (\"B\", \"Z\"),\n",
    "        (\"B\", \"Y\"),\n",
    "        (\"Z\", \"Y\"),\n",
    "        (\"X\", \"Y\"),\n",
    "        (\"C\", \"X\"),\n",
    "        (\"C\", \"Y\")\n",
    "    ],\n",
    "    edge_props={\n",
    "        (\"Z\", \"X\"): {\"color\": \"red\", \"label\": \"XZY\", \"fontcolor\": \"red\"},\n",
    "        (\"Z\", \"Y\"): {\"color\": \"red\"},\n",
    "    },\n",
    "    graph_direction=\"LR\"\n",
    ")"
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  },
  {
   "cell_type": "markdown",
   "id": "e0a7b311-472c-49b2-a282-55a42d58cb72",
   "metadata": {},
   "source": [
    "#### $X \\leftarrow Z \\rightarrow Y$\n",
    "- Backdoor non-causal path\n",
    "- Block by **stratifying by $Z$**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "8365fbb9-8408-4a4a-9402-3ccc0c7a149f",
   "metadata": {},
   "outputs": [
    {
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     "execution_count": 19,
     "metadata": {},
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   ],
   "source": [
    "utils.draw_causal_graph(\n",
    "    edge_list=[\n",
    "        (\"A\", \"Z\"),\n",
    "        (\"A\", \"X\"),\n",
    "        (\"Z\", \"X\"),\n",
    "        (\"B\", \"Z\"),\n",
    "        (\"B\", \"Y\"),\n",
    "        (\"Z\", \"Y\"),\n",
    "        (\"X\", \"Y\"),\n",
    "        (\"C\", \"X\"),\n",
    "        (\"C\", \"Y\")\n",
    "    ],\n",
    "    edge_props={\n",
    "        (\"A\", \"X\"): {\"color\": \"red\", \"label\": \"XAZBY\", \"fontcolor\": \"red\"},\n",
    "        (\"A\", \"Z\"): {\"color\": \"red\"},\n",
    "        (\"B\", \"Z\"): {\"color\": \"red\"},\n",
    "        (\"B\", \"Y\"): {\"color\": \"red\"},\n",
    "        \n",
    "    },\n",
    "    graph_direction=\"LR\"\n",
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  },
  {
   "cell_type": "markdown",
   "id": "1abf64e5-f16a-4498-aa82-835e57535bb9",
   "metadata": {},
   "source": [
    "#### $X \\leftarrow A \\rightarrow Z \\leftarrow B \\rightarrow Y$\n",
    "- Backdoor non-causal path\n",
    "- Block by **stratifying by $A$ or $B$**; stratifying by $Z$ opens the path b.c. it's a collider\n",
    "  - we're already stratifying by $Z$ for the $X,Z,Y$ backdoor path"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "86e219fb-de71-448c-b647-5b757dfd436c",
   "metadata": {},
   "outputs": [
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       "</g>\n",
       "<!-- B&#45;&gt;Z -->\n",
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       "</g>\n",
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       "<title>C&#45;&gt;X</title>\n",
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       "<title>C&#45;&gt;Y</title>\n",
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       "</g>\n",
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      "text/plain": [
       "<graphviz.graphs.Digraph at 0x11e6e6c50>"
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     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "utils.draw_causal_graph(\n",
    "    edge_list=[\n",
    "        (\"A\", \"Z\"),\n",
    "        (\"A\", \"X\"),\n",
    "        (\"Z\", \"X\"),\n",
    "        (\"B\", \"Z\"),\n",
    "        (\"B\", \"Y\"),\n",
    "        (\"Z\", \"Y\"),\n",
    "        (\"X\", \"Y\"),\n",
    "        (\"C\", \"X\"),\n",
    "        (\"C\", \"Y\")\n",
    "    ],\n",
    "    edge_props={\n",
    "        (\"A\", \"X\"): {\"color\": \"red\", \"label\": \"XAZY\", \"fontcolor\": \"red\"},\n",
    "        (\"A\", \"Z\"): {\"color\": \"red\"},\n",
    "        (\"Z\", \"Y\"): {\"color\": \"red\"},\n",
    "        (\"Z\", \"Y\"): {\"color\": \"red\"},\n",
    "        \n",
    "    },\n",
    "    graph_direction=\"LR\"\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "888b885b-c066-4d31-888d-38fb706d418a",
   "metadata": {},
   "source": [
    "#### $X \\leftarrow A \\rightarrow Z \\rightarrow Y$\n",
    "- Backdoor non-causal path\n",
    "- Block by **stratifying by $A$;** stratifying by $Z$ opens the path b.c. it's a collider\n",
    "  - we're already stratifying by $Z$ for the $X \\leftarrow Z \\rightarrow Y$ backdoor path"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "69def1ed-9f34-4064-932c-5f5e3a55ee01",
   "metadata": {},
   "outputs": [
    {
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       "<title>C</title>\n",
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       "<text text-anchor=\"middle\" x=\"118\" y=\"-29.13\" font-family=\"Times,serif\" font-size=\"14.00\">C</text>\n",
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       "<!-- C&#45;&gt;X -->\n",
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     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "utils.draw_causal_graph(\n",
    "    edge_list=[\n",
    "        (\"A\", \"Z\"),\n",
    "        (\"A\", \"X\"),\n",
    "        (\"Z\", \"X\"),\n",
    "        (\"B\", \"Z\"),\n",
    "        (\"B\", \"Y\"),\n",
    "        (\"Z\", \"Y\"),\n",
    "        (\"X\", \"Y\"),\n",
    "        (\"C\", \"X\"),\n",
    "        (\"C\", \"Y\")\n",
    "    ],\n",
    "    edge_props={\n",
    "        (\"Z\", \"X\"): {\"color\": \"red\", \"label\": \"XZBY\", \"fontcolor\": \"red\"},\n",
    "        (\"B\", \"Z\"): {\"color\": \"red\"},\n",
    "        (\"B\", \"Y\"): {\"color\": \"red\"},\n",
    "    },\n",
    "    graph_direction=\"LR\"\n",
    ")"
   ]
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  {
   "cell_type": "markdown",
   "id": "90879f15-aa17-4b51-8eda-c1071787cbe5",
   "metadata": {},
   "source": [
    "#### $X \\leftarrow Z \\leftarrow B \\rightarrow Y$\n",
    "- Backdoor non-causal path\n",
    "- Block by **stratifying by $B$**; stratifying by $Z$ opens the path b.c. it's a collider\n",
    "  - we're already stratifying by $Z$ for the $X \\leftarrow Z \\rightarrow Y$ backdoor path"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f63c5a3c-1706-47b9-8cb4-75dc378fa308",
   "metadata": {},
   "source": [
    "#### Resulting Minimum Adjustment set: Z, C, (A or B)\n",
    "Chossing B over A turns out to be more statistically efficient, though not causally different than choosing A"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "63944882-aa94-43eb-8329-68bdcf55714e",
   "metadata": {},
   "source": [
    "## Example with unobserved confounds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "b085c2f6-fbd2-4b97-b219-3e90ec7ecc33",
   "metadata": {},
   "outputs": [
    {
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       "<title>P&#45;&gt;C</title>\n",
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       "<title>unobserved</title>\n",
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     "execution_count": 22,
     "metadata": {},
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   ],
   "source": [
    "utils.draw_causal_graph(\n",
    "    edge_list=[\n",
    "        (\"G\", \"P\"),\n",
    "        (\"P\", \"C\"),\n",
    "        (\"G\", \"C\"),\n",
    "        (\"u\", \"P\"),\n",
    "        (\"u\", \"C\")\n",
    "    ],\n",
    "    node_props={\n",
    "        \"G\": {\"color\": \"blue\"},\n",
    "        \"C\": {\"color\": \"blue\"},\n",
    "        \"P\": {\"color\": \"red\", \"label\": \"collider, P\"},\n",
    "        \"u\": {\"style\": \"dashed\"},\n",
    "        \"unobserved\": {\"style\": \"dashed\"}\n",
    "    },\n",
    "    edge_props={\n",
    "        (\"G\", \"C\"): {\"color\": \"blue\"}\n",
    "    },\n",
    "    graph_direction=\"LR\"\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b5e37c20-b75d-4144-89d5-cae0b99c2853",
   "metadata": {},
   "source": [
    "- $P$ is mediator between $G$ and $C$\n",
    "- $P$ is also a collider between $G, u, C$\n",
    "- If we want to estimate direct effect of $G \\rightarrow C$, we'll need to stratify by $P$--close the Pipe\n",
    "- However, this will open up the Collider path to the unobserved confound.\n",
    "- **It's not possible to accurately estimate the _Direct Causal Effect_ of G on C**\n",
    "- It **is possible to estimate the _Total Causal Effect_**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4e9ab25e",
   "metadata": {},
   "source": [
    "# Good and bad controls\n",
    "\n",
    "## Common _incorrect_ heuristics for choosing control variables:\n",
    "\n",
    "- YOLO approach -- anything in the spreadsheet\n",
    "- Ignore highly colinear variables\n",
    "  - false, no support for this\n",
    "  - colinearity can arise through many different causal processes that _can be modeled accurately_\n",
    "- It's safe to add pre-treatment variables\n",
    "  - false, pre-treatment, just like post-treatment variables can cause confounds."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f95175c1",
   "metadata": {},
   "source": [
    "## Good & Bad Controls Examples"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3fdbe678-4677-4e72-97a3-2ed2fa5a531c",
   "metadata": {},
   "source": [
    "### Bad control"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "afaf1af1",
   "metadata": {},
   "outputs": [
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     "execution_count": 23,
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   ],
   "source": [
    "utils.draw_causal_graph(\n",
    "    edge_list=[\n",
    "        (\"u\", \"Z\"),\n",
    "        (\"v\", \"Z\"),\n",
    "        (\"u\", \"X\"),\n",
    "        (\"X\", 'Y'),\n",
    "        (\"v\", \"Y\")\n",
    "    ],\n",
    "    node_props={\n",
    "        \"u\": {\"style\": \"dashed\"},\n",
    "        \"v\": {\"style\": \"dashed\"},\n",
    "        \"X\": {\"color\": \"red\"},\n",
    "        \"Y\": {\"color\": \"red\"},\n",
    "        \"unobserved\": {\"style\": \"dashed\"},\n",
    "    },\n",
    "    edge_props={\n",
    "        (\"X\", \"Y\"): {\"color\": \"red\"}\n",
    "    },\n",
    "    graph_direction=\"TD\"\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c9ef216b",
   "metadata": {},
   "source": [
    "$Z$ is a collider for unobserved variables $u$ and $v$, which independently affect $X$ and $Y$\n",
    "#### List the paths\n",
    "- $X \\rightarrow Y$\n",
    "  - causal, leave open\n",
    "- $X \\leftarrow u \\rightarrow Z \\leftarrow v \\rightarrow Y$\n",
    "  - backdoor, closed due to collider\n",
    "  - $Z$ is a **bad control**: stratifying by $Z$ would open the backdoor path\n",
    "  - $Z$ could be a pre-treatment variable -- not always good to stratify by pre-treatment variables; **draw your causal assumptions**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f156a2cf",
   "metadata": {},
   "source": [
    "### Bad mediator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "00a4ed24",
   "metadata": {},
   "outputs": [
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       "<title>X&#45;&gt;Z</title>\n",
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       "<title>Z&#45;&gt;Y</title>\n",
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       "</g>\n",
       "<!-- u&#45;&gt;Z -->\n",
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       "<title>u&#45;&gt;Z</title>\n",
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       "<title>unobserved</title>\n",
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     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "utils.draw_causal_graph(\n",
    "    edge_list=[\n",
    "        (\"X\", \"Z\"),\n",
    "        (\"Z\", \"Y\"),\n",
    "        (\"u\", \"Z\"),\n",
    "        (\"u\", \"Y\"),\n",
    "    ],\n",
    "    node_props={\n",
    "        \"X\": {\"color\": \"red\"},\n",
    "        \"Y\": {\"color\": \"red\"},\n",
    "        \"u\": {\"style\": \"dashed\"},\n",
    "        \"unobserved\": {\"style\": \"dashed\"},\n",
    "    },\n",
    "    edge_props={\n",
    "        (\"X\", \"Z\"): {\"color\": \"red\"},\n",
    "        (\"Z\", \"Y\"): {\"color\": \"red\"}\n",
    "    },\n",
    "    graph_direction='LR'\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7c9ac354",
   "metadata": {},
   "source": [
    "#### List the paths\n",
    "- $X \\rightarrow Z \\rightarrow Y$\n",
    "  - causal, leave open\n",
    "- $X \\rightarrow Z \\leftarrow u \\rightarrow Y$\n",
    "  - backdoor, but only if stratifying by Z\n",
    "- There **is no backdoor path, so no need to stratify by Z**\n",
    "- Can measure total effect of $X$ on $Y$, but not direct effect, because of Mediatior $Z$"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a96afd09",
   "metadata": {},
   "source": [
    "No backdoor path here, so no need to control for any confounds. In fact, stratifying by `Z` (the bad mediator) will introduce bias in estimate because it introduces the causal effect of `u` that would otherwise be blocked.\n",
    "\n",
    "`Z` is often a post-treatment variable, e.g. below, where \"Happiness\" is affected by the treatment \"Win Lottery\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3dceb094",
   "metadata": {},
   "source": [
    "### Verify bad mediatior with simulation:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "29f99386",
   "metadata": {},
   "outputs": [
    {
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   "source": [
    "utils.draw_causal_graph(\n",
    "    edge_list=[\n",
    "        (\"X\", \"Z\"),\n",
    "        (\"Z\", \"Y\"),\n",
    "        (\"u\", \"Z\"),\n",
    "        (\"u\", \"Y\"),\n",
    "    ],\n",
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    "        (\"Z\", \"Y\"): {\"color\": \"red\", \"label\": \"1\"},\n",
    "        (\"u\", \"Z\"): {\"label\": \"1\"},\n",
    "        (\"u\", \"Y\"): {\"label\": \"1\"}\n",
    "    },\n",
    "    graph_direction='LR'\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "f278815d",
   "metadata": {},
   "outputs": [],
   "source": [
    "def simulate_bad_mediator(beta_XZ, beta_ZY, n_samples=100):\n",
    "    # independent variables\n",
    "    u = stats.norm.rvs(size=n_samples)\n",
    "    X = stats.norm.rvs(size=n_samples)\n",
    "    \n",
    "    # causal effect of X on Z\n",
    "    mu_Z = X * beta_XZ + u\n",
    "    Z = stats.norm.rvs(size=n_samples, loc=mu_Z)\n",
    "    \n",
    "    # Causal effect of Z on Y (including confound)\n",
    "    mu_Y = Z * beta_ZY + u\n",
    "    Y = stats.norm.rvs(size=n_samples, loc=mu_Y)\n",
    "    \n",
    "    # Put data into format for statsmodels\n",
    "    data = pd.DataFrame(np.vstack([Y, X, Z, u]).T, columns=[\"Y\", \"X\", \"Z\", \"u\"])\n",
    "    \n",
    "    unstratified_model = smf.ols(\"Y ~ X\", data=data).fit()\n",
    "    stratified_model = smf.ols(\"Y ~ X + Z\", data=data).fit()\n",
    "    \n",
    "    return unstratified_model.params.X, stratified_model.params.X\n",
    "\n",
    "def run_bad_mediator_simulation(beta_XZ=1, beta_ZY=1, n_simulations=500, n_samples_per_simualtion=100):\n",
    "    beta_X = beta_XZ * beta_ZY\n",
    "\n",
    "    simulations = np.array(\n",
    "        [\n",
    "            simulate_bad_mediator(beta_XZ=beta_XZ, beta_ZY=beta_ZY, n_samples=n_samples_per_simualtion) \n",
    "            for _ in range(n_simulations)\n",
    "        ]\n",
    "    )\n",
    "    _, ax = plt.subplots(figsize=(8, 4))\n",
    "    az.plot_dist(simulations[:, 0], label='Y ~ X\\ncorrect', color='black', ax=ax)\n",
    "    az.plot_dist(simulations[:, 1], label='Y ~ X + Z\\nwrong', color=\"C0\", ax=ax)\n",
    "    plt.axvline(beta_X, color='k', linestyle='--', label=f'actual={beta_X}')\n",
    "    plt.legend(loc='upper left')\n",
    "    plt.xlabel(\"posterior mean\")\n",
    "    plt.ylabel(\"density\");"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b051efac-af2b-4017-9861-6c22559951ad",
   "metadata": {},
   "source": [
    "#### Run the simulation, $\\beta_{XZ} = \\beta_{ZY} = 1$ "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "afdc37c2-e2ac-42ee-a2ab-f16eea466fad",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "run_bad_mediator_simulation(beta_XZ=1, beta_ZY=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "63141a0b-8e06-4641-9f66-8f8ffe6642b3",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<g id=\"node4\" class=\"node\">\n",
       "<title>u</title>\n",
       "<ellipse fill=\"none\" stroke=\"black\" stroke-dasharray=\"5,2\" cx=\"53.4\" cy=\"-18\" rx=\"27\" ry=\"18\"/>\n",
       "<text text-anchor=\"middle\" x=\"53.4\" y=\"-12.95\" font-family=\"Times,serif\" font-size=\"14.00\">u</text>\n",
       "</g>\n",
       "<!-- u&#45;&gt;Z -->\n",
       "<g id=\"edge3\" class=\"edge\">\n",
       "<title>u&#45;&gt;Z</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M77.76,-26.29C96.03,-32.77 121.67,-41.88 142.11,-49.13\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"140.67,-52.69 151.26,-52.73 143.01,-46.09 140.67,-52.69\"/>\n",
       "<text text-anchor=\"middle\" x=\"128.17\" y=\"-47.2\" font-family=\"Times,serif\" font-size=\"14.00\">1</text>\n",
       "</g>\n",
       "<!-- u&#45;&gt;Y -->\n",
       "<g id=\"edge4\" class=\"edge\">\n",
       "<title>u&#45;&gt;Y</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M80.82,-17.11C110.64,-16.29 160.61,-15.47 203.55,-17.5 213.92,-17.99 225.15,-18.89 235.47,-19.87\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"234.92,-23.44 245.22,-20.95 235.62,-16.47 234.92,-23.44\"/>\n",
       "<text text-anchor=\"middle\" x=\"176.55\" y=\"-20.2\" font-family=\"Times,serif\" font-size=\"14.00\">1</text>\n",
       "</g>\n",
       "<!-- unobserved -->\n",
       "<g id=\"node5\" class=\"node\">\n",
       "<title>unobserved</title>\n",
       "<ellipse fill=\"none\" stroke=\"black\" stroke-dasharray=\"5,2\" cx=\"53.4\" cy=\"-135\" rx=\"53.4\" ry=\"18\"/>\n",
       "<text text-anchor=\"middle\" x=\"53.4\" y=\"-129.95\" font-family=\"Times,serif\" font-size=\"14.00\">unobserved</text>\n",
       "</g>\n",
       "</g>\n",
       "</svg>\n"
      ],
      "text/plain": [
       "<graphviz.graphs.Digraph at 0x11f9bff10>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "utils.draw_causal_graph(\n",
    "    edge_list=[\n",
    "        (\"X\", \"Z\"),\n",
    "        (\"Z\", \"Y\"),\n",
    "        (\"u\", \"Z\"),\n",
    "        (\"u\", \"Y\"),\n",
    "    ],\n",
    "    node_props={\n",
    "        \"X\": {\"color\": \"red\"},\n",
    "        \"Y\": {\"color\": \"red\"},\n",
    "        \"u\": {\"style\": \"dashed\"},\n",
    "        \"unobserved\": {\"style\": \"dashed\"},\n",
    "    },\n",
    "    edge_props={\n",
    "        (\"X\", \"Z\"): {\"color\": \"red\", \"label\": \"1\"},\n",
    "        (\"Z\", \"Y\"): {\"color\": \"red\", \"label\": \"0\"},\n",
    "        (\"u\", \"Z\"): {\"label\": \"1\"},\n",
    "        (\"u\", \"Y\"): {\"label\": \"1\"}\n",
    "    },\n",
    "    graph_direction='LR'\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c4cb6a05-5445-4e23-aaf9-58f1aebde3f1",
   "metadata": {},
   "source": [
    "#### Turn off Causal effect by changing $\\beta_{ZY}$ to 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "780bdbcd-df3f-4bf4-bc74-22bc704f285d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "run_bad_mediator_simulation(beta_XZ=1, beta_ZY=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9bcf031e-d74e-40e0-9c84-db7737d8df11",
   "metadata": {},
   "source": [
    "though there is no causal effect, you end up concluding a negative effect of X on Y"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bcc8d701",
   "metadata": {},
   "source": [
    "## Colliders & Descendants\n",
    "\n",
    "Generally, **Avoid the Collider**!\n",
    "\n",
    "Adding descendants of the target variable is almost always a terrible idea, because your selecting groups based on the outcome. This is known as **Case Control Bias** (selection on outcome)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "c43f34e6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/svg+xml": [
       "<?xml version=\"1.0\" encoding=\"UTF-8\" standalone=\"no\"?>\n",
       "<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n",
       " \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\n",
       "<!-- Generated by graphviz version 8.1.0 (0)\n",
       " -->\n",
       "<!-- Pages: 1 -->\n",
       "<svg width=\"89pt\" height=\"188pt\"\n",
       " viewBox=\"0.00 0.00 89.00 188.00\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n",
       "<g id=\"graph0\" class=\"graph\" transform=\"scale(1 1) rotate(0) translate(4 184)\">\n",
       "<polygon fill=\"white\" stroke=\"none\" points=\"-4,4 -4,-184 85,-184 85,4 -4,4\"/>\n",
       "<!-- X -->\n",
       "<g id=\"node1\" class=\"node\">\n",
       "<title>X</title>\n",
       "<ellipse fill=\"none\" stroke=\"red\" cx=\"54\" cy=\"-162\" rx=\"27\" ry=\"18\"/>\n",
       "<text text-anchor=\"middle\" x=\"54\" y=\"-156.95\" font-family=\"Times,serif\" font-size=\"14.00\">X</text>\n",
       "</g>\n",
       "<!-- Y -->\n",
       "<g id=\"node2\" class=\"node\">\n",
       "<title>Y</title>\n",
       "<ellipse fill=\"none\" stroke=\"red\" cx=\"27\" cy=\"-90\" rx=\"27\" ry=\"18\"/>\n",
       "<text text-anchor=\"middle\" x=\"27\" y=\"-84.95\" font-family=\"Times,serif\" font-size=\"14.00\">Y</text>\n",
       "</g>\n",
       "<!-- X&#45;&gt;Y -->\n",
       "<g id=\"edge1\" class=\"edge\">\n",
       "<title>X&#45;&gt;Y</title>\n",
       "<path fill=\"none\" stroke=\"red\" d=\"M47.46,-144.05C44.45,-136.23 40.79,-126.75 37.41,-117.98\"/>\n",
       "<polygon fill=\"red\" stroke=\"red\" points=\"40.34,-116.86 33.48,-108.79 33.81,-119.38 40.34,-116.86\"/>\n",
       "</g>\n",
       "<!-- Z -->\n",
       "<g id=\"node3\" class=\"node\">\n",
       "<title>Z</title>\n",
       "<ellipse fill=\"none\" stroke=\"black\" cx=\"54\" cy=\"-18\" rx=\"27\" ry=\"18\"/>\n",
       "<text text-anchor=\"middle\" x=\"54\" y=\"-12.95\" font-family=\"Times,serif\" font-size=\"14.00\">Z</text>\n",
       "</g>\n",
       "<!-- X&#45;&gt;Z -->\n",
       "<g id=\"edge2\" class=\"edge\">\n",
       "<title>X&#45;&gt;Z</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M57.65,-143.91C59.68,-133.57 61.98,-120.09 63,-108 64.34,-92.06 64.34,-87.94 63,-72 62.31,-63.78 61.02,-54.92 59.63,-46.85\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"62.91,-46.29 57.65,-37.09 56.02,-47.56 62.91,-46.29\"/>\n",
       "</g>\n",
       "<!-- Y&#45;&gt;Z -->\n",
       "<g id=\"edge3\" class=\"edge\">\n",
       "<title>Y&#45;&gt;Z</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M33.54,-72.05C36.55,-64.23 40.21,-54.75 43.59,-45.98\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"47.19,-47.38 47.52,-36.79 40.66,-44.86 47.19,-47.38\"/>\n",
       "</g>\n",
       "</g>\n",
       "</svg>\n"
      ],
      "text/plain": [
       "<graphviz.graphs.Digraph at 0x11fbb3610>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "utils.draw_causal_graph(\n",
    "    edge_list=[\n",
    "        (\"X\", \"Y\"),\n",
    "        (\"X\", \"Z\"),\n",
    "        (\"Y\", \"Z\")\n",
    "    ],\n",
    "    node_props={\n",
    "        \"X\": {\"color\": \"red\"},\n",
    "        \"Y\": {\"color\": \"red\"}\n",
    "    },\n",
    "    edge_props={\n",
    "        (\"X\", \"Y\"): {\"color\": \"red\"}\n",
    "    }\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ab5e1996",
   "metadata": {},
   "source": [
    "Colliders not always so obvious"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "5010ba68",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/svg+xml": [
       "<?xml version=\"1.0\" encoding=\"UTF-8\" standalone=\"no\"?>\n",
       "<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n",
       " \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\n",
       "<!-- Generated by graphviz version 8.1.0 (0)\n",
       " -->\n",
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     "execution_count": 31,
     "metadata": {},
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   "source": [
    "utils.draw_causal_graph(\n",
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    "        (\"u\", \"Y\"),\n",
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    "    }\n",
    ")"
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   "cell_type": "markdown",
   "id": "6a95792a",
   "metadata": {},
   "source": [
    "Collider is formed by unobserved variable u"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "293d7935",
   "metadata": {},
   "source": [
    "### Bad Descendent: Selection on Outcome (Case Control Bias)\n",
    "\n",
    "Stratifying on a variable affected by the outcome is a **very bad** practice.\n",
    "- reduces variation in $Y$ that could have been explained by $X$\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "6b157c1a",
   "metadata": {},
   "outputs": [
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   "source": [
    "utils.draw_causal_graph(\n",
    "    edge_list=[\n",
    "        (\"X\", \"Y\"),\n",
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    "    ],\n",
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  {
   "cell_type": "markdown",
   "id": "03834a5d",
   "metadata": {},
   "source": [
    "#### Verify via simulation:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "d724b1c7",
   "metadata": {},
   "outputs": [
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   "source": [
    "utils.draw_causal_graph(\n",
    "    edge_list=[\n",
    "        (\"X\", \"Y\"),\n",
    "        (\"Y\", \"Z\"),\n",
    "    ],\n",
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    "    graph_direction=\"LR\"\n",
    ")"
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  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "8d5f1927",
   "metadata": {},
   "outputs": [],
   "source": [
    "def simulate_case_control_bias(beta_XY=1, beta_YZ=1, n_samples=100):\n",
    "    # independent variables\n",
    "    X = stats.norm.rvs(size=n_samples)\n",
    "    \n",
    "    # Causal effect of Z on Y (including confound)\n",
    "    mu_Y = X * beta_XY\n",
    "    Y = stats.norm.rvs(size=n_samples, loc=mu_Y)\n",
    "    \n",
    "    # causal effect of X on Z\n",
    "    mu_Z = Y * beta_YZ\n",
    "    Z = stats.norm.rvs(size=n_samples, loc=mu_Z)\n",
    "\n",
    "    \n",
    "    # Put data into format for statsmodels\n",
    "    data = pd.DataFrame(np.vstack([Y, X, Z]).T, columns=[\"Y\", \"X\", \"Z\"])\n",
    "    \n",
    "    unstratified_model = smf.ols(\"Y ~ X\", data=data).fit()\n",
    "    stratified_model = smf.ols(\"Y ~ X + Z\", data=data).fit()\n",
    "    \n",
    "    return unstratified_model.params.X, stratified_model.params.X\n",
    "\n",
    "def run_case_control_simulation(beta_XY=1, beta_YZ=1, n_simulations=500, n_samples_per_simualtion=100):\n",
    "    beta_X = beta_XY\n",
    "\n",
    "    simulations = np.array(\n",
    "        [\n",
    "            simulate_case_control_bias(beta_XY=beta_XY, beta_YZ=beta_YZ, n_samples=n_samples_per_simualtion) \n",
    "            for _ in range(n_simulations)\n",
    "        ]\n",
    "    )\n",
    "    _, ax = plt.subplots(figsize=(8, 4))\n",
    "    az.plot_dist(simulations[:, 0], label='Y ~ X\\ncorrect', color='black', ax=ax)\n",
    "    az.plot_dist(simulations[:, 1], label='Y ~ X + Z\\nwrong', color=\"C0\", ax=ax)\n",
    "    plt.axvline(beta_X, color='k', linestyle='--', label=f'actual={beta_X}')\n",
    "    plt.legend(loc='upper left')\n",
    "    plt.xlabel(\"posterior mean\")\n",
    "    plt.ylabel(\"density\");"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "55d5fe6b-6fe5-4683-8747-d00467894cb1",
   "metadata": {},
   "source": [
    "#### Descendant explains away some of he causal effect of $X$ on $Y$\n",
    "The estimated causal effect has been reduced because the descendent reduces the variation in $Y$ that can can be explained by $X$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "b2ced62d-c67a-49af-a90f-25389fb73286",
   "metadata": {},
   "outputs": [
    {
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",
      "text/plain": [
       "<Figure size 800x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "run_case_control_simulation(beta_XY=1, beta_YZ=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "26c1859b-d03b-4050-aecd-3a80e9b44bda",
   "metadata": {},
   "source": [
    "### Removing Descendent effect by setting $\\beta_{YZ}=0$\n",
    "The descendant no longer has any effect here, so we should recover the same (correct) inference for both stratified and unstratified models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "7eedfcf6-4096-479b-b743-460a69fcf26f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "run_case_control_simulation(beta_XY=1, beta_YZ=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0f2fcae2",
   "metadata": {},
   "source": [
    "### Bad Ancestor (aka precision parasite)\n",
    "\n",
    "Now $Z$ is a parent of $X$\n",
    "\n",
    "- no backdoor path, $X$ is directly connected to $Y$\n",
    "- when stratifying by $Z$ you're explaining away variation in $X$, thus reducing the amount of causal information between $X$ and $Y$ that can be explained otherwise.\n",
    "- **Does not bias your estimate,** but it  **reduces precision**, so estimates will have more uncertainty"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "ee2dc9c6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/svg+xml": [
       "<?xml version=\"1.0\" encoding=\"UTF-8\" standalone=\"no\"?>\n",
       "<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n",
       " \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\n",
       "<!-- Generated by graphviz version 8.1.0 (0)\n",
       " -->\n",
       "<!-- Pages: 1 -->\n",
       "<svg width=\"256pt\" height=\"44pt\"\n",
       " viewBox=\"0.00 0.00 255.50 44.00\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n",
       "<g id=\"graph0\" class=\"graph\" transform=\"scale(1 1) rotate(0) translate(4 40)\">\n",
       "<polygon fill=\"white\" stroke=\"none\" points=\"-4,4 -4,-40 251.5,-40 251.5,4 -4,4\"/>\n",
       "<!-- X -->\n",
       "<g id=\"node1\" class=\"node\">\n",
       "<title>X</title>\n",
       "<ellipse fill=\"none\" stroke=\"red\" cx=\"123.75\" cy=\"-18\" rx=\"27\" ry=\"18\"/>\n",
       "<text text-anchor=\"middle\" x=\"123.75\" y=\"-12.95\" font-family=\"Times,serif\" font-size=\"14.00\">X</text>\n",
       "</g>\n",
       "<!-- Y -->\n",
       "<g id=\"node2\" class=\"node\">\n",
       "<title>Y</title>\n",
       "<ellipse fill=\"none\" stroke=\"red\" cx=\"220.5\" cy=\"-18\" rx=\"27\" ry=\"18\"/>\n",
       "<text text-anchor=\"middle\" x=\"220.5\" y=\"-12.95\" font-family=\"Times,serif\" font-size=\"14.00\">Y</text>\n",
       "</g>\n",
       "<!-- X&#45;&gt;Y -->\n",
       "<g id=\"edge1\" class=\"edge\">\n",
       "<title>X&#45;&gt;Y</title>\n",
       "<path fill=\"none\" stroke=\"red\" d=\"M151.14,-18C160.79,-18 171.9,-18 182.32,-18\"/>\n",
       "<polygon fill=\"red\" stroke=\"red\" points=\"182.25,-21.5 192.25,-18 182.25,-14.5 182.25,-21.5\"/>\n",
       "<text text-anchor=\"middle\" x=\"172.12\" y=\"-21.2\" font-family=\"Times,serif\" font-size=\"14.00\">1</text>\n",
       "</g>\n",
       "<!-- Z -->\n",
       "<g id=\"node3\" class=\"node\">\n",
       "<title>Z</title>\n",
       "<ellipse fill=\"none\" stroke=\"black\" cx=\"27\" cy=\"-18\" rx=\"27\" ry=\"18\"/>\n",
       "<text text-anchor=\"middle\" x=\"27\" y=\"-12.95\" font-family=\"Times,serif\" font-size=\"14.00\">Z</text>\n",
       "</g>\n",
       "<!-- Z&#45;&gt;X -->\n",
       "<g id=\"edge2\" class=\"edge\">\n",
       "<title>Z&#45;&gt;X</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M54.39,-18C64.04,-18 75.15,-18 85.57,-18\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"85.5,-21.5 95.5,-18 85.5,-14.5 85.5,-21.5\"/>\n",
       "<text text-anchor=\"middle\" x=\"75.38\" y=\"-21.2\" font-family=\"Times,serif\" font-size=\"14.00\">1</text>\n",
       "</g>\n",
       "</g>\n",
       "</svg>\n"
      ],
      "text/plain": [
       "<graphviz.graphs.Digraph at 0x11e1b82b0>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "utils.draw_causal_graph(\n",
    "    edge_list=[\n",
    "        (\"X\", \"Y\"),\n",
    "        (\"Z\", \"X\"),\n",
    "    ],\n",
    "    node_props={\n",
    "        \"X\": {\"color\": \"red\"},\n",
    "        \"Y\": {\"color\": \"red\"},\n",
    "    },\n",
    "    edge_props={\n",
    "        (\"X\", \"Y\"): {\"color\": \"red\", \"label\": \"1\"},\n",
    "        (\"Z\", \"X\"): {\"label\": \"1\"}\n",
    "    },\n",
    "    graph_direction=\"LR\"\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4fdb6677",
   "metadata": {},
   "source": [
    "#### Verify via simulation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "5eb6d0d7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/svg+xml": [
       "<?xml version=\"1.0\" encoding=\"UTF-8\" standalone=\"no\"?>\n",
       "<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n",
       " \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\n",
       "<!-- Generated by graphviz version 8.1.0 (0)\n",
       " -->\n",
       "<!-- Pages: 1 -->\n",
       "<svg width=\"250pt\" height=\"44pt\"\n",
       " viewBox=\"0.00 0.00 249.75 44.00\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n",
       "<g id=\"graph0\" class=\"graph\" transform=\"scale(1 1) rotate(0) translate(4 40)\">\n",
       "<polygon fill=\"white\" stroke=\"none\" points=\"-4,4 -4,-40 245.75,-40 245.75,4 -4,4\"/>\n",
       "<!-- X -->\n",
       "<g id=\"node1\" class=\"node\">\n",
       "<title>X</title>\n",
       "<ellipse fill=\"none\" stroke=\"red\" cx=\"118\" cy=\"-18\" rx=\"27\" ry=\"18\"/>\n",
       "<text text-anchor=\"middle\" x=\"118\" y=\"-12.95\" font-family=\"Times,serif\" font-size=\"14.00\">X</text>\n",
       "</g>\n",
       "<!-- Y -->\n",
       "<g id=\"node2\" class=\"node\">\n",
       "<title>Y</title>\n",
       "<ellipse fill=\"none\" stroke=\"red\" cx=\"214.75\" cy=\"-18\" rx=\"27\" ry=\"18\"/>\n",
       "<text text-anchor=\"middle\" x=\"214.75\" y=\"-12.95\" font-family=\"Times,serif\" font-size=\"14.00\">Y</text>\n",
       "</g>\n",
       "<!-- X&#45;&gt;Y -->\n",
       "<g id=\"edge1\" class=\"edge\">\n",
       "<title>X&#45;&gt;Y</title>\n",
       "<path fill=\"none\" stroke=\"red\" d=\"M145.39,-18C155.04,-18 166.15,-18 176.57,-18\"/>\n",
       "<polygon fill=\"red\" stroke=\"red\" points=\"176.5,-21.5 186.5,-18 176.5,-14.5 176.5,-21.5\"/>\n",
       "<text text-anchor=\"middle\" x=\"166.38\" y=\"-21.2\" font-family=\"Times,serif\" font-size=\"14.00\">1</text>\n",
       "</g>\n",
       "<!-- Z -->\n",
       "<g id=\"node3\" class=\"node\">\n",
       "<title>Z</title>\n",
       "<ellipse fill=\"none\" stroke=\"black\" cx=\"27\" cy=\"-18\" rx=\"27\" ry=\"18\"/>\n",
       "<text text-anchor=\"middle\" x=\"27\" y=\"-12.95\" font-family=\"Times,serif\" font-size=\"14.00\">Z</text>\n",
       "</g>\n",
       "<!-- Z&#45;&gt;X -->\n",
       "<g id=\"edge2\" class=\"edge\">\n",
       "<title>Z&#45;&gt;X</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M54.22,-18C62.21,-18 71.14,-18 79.71,-18\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"79.66,-21.5 89.66,-18 79.66,-14.5 79.66,-21.5\"/>\n",
       "</g>\n",
       "</g>\n",
       "</svg>\n"
      ],
      "text/plain": [
       "<graphviz.graphs.Digraph at 0x11fbb35b0>"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "utils.draw_causal_graph(\n",
    "    edge_list=[\n",
    "        (\"X\", \"Y\"),\n",
    "        (\"Z\", \"X\"),\n",
    "    ],\n",
    "    node_props={\n",
    "        \"X\": {\"color\": \"red\"},\n",
    "        \"Y\": {\"color\": \"red\"},\n",
    "    },\n",
    "    edge_props={\n",
    "        (\"X\", \"Y\"): {\"color\": \"red\", \"label\": \"1\"},\n",
    "        (\"Y\", \"Z\"): {\"color\": \"red\", \"label\": \"1\"}\n",
    "    },\n",
    "    graph_direction=\"LR\"\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "b8abf3f8",
   "metadata": {},
   "outputs": [],
   "source": [
    "np.random.seed(123)\n",
    "def simulate_bad_ancestor(beta_ZX=1, beta_XY=1, unobserved_variance=None, n_samples=100):\n",
    "    Z = stats.norm.rvs(size=n_samples)\n",
    "    \n",
    "    mu_X = Z * beta_ZX\n",
    "    if unobserved_variance is not None:\n",
    "        u = stats.norm.rvs(size=n_samples) * unobserved_variance\n",
    "        mu_X += u\n",
    "\n",
    "    X = stats.norm.rvs(size=n_samples, loc=mu_X)\n",
    "    \n",
    "    mu_Y = X * beta_XY\n",
    "    if unobserved_variance is not None:\n",
    "        mu_Y += u\n",
    "        \n",
    "    Y = stats.norm.rvs(size=n_samples, loc=mu_Y)\n",
    "    \n",
    "    data = pd.DataFrame(np.vstack([X, Y, Z]).T, columns=['X', 'Y', 'Z'])\n",
    "    \n",
    "    non_stratified_model = smf.ols(\"Y ~ X\", data=data).fit()\n",
    "    stratified_model = smf.ols(\"Y ~ X + Z\", data=data).fit()\n",
    "    \n",
    "    return non_stratified_model.params.X, stratified_model.params.X\n",
    "\n",
    "\n",
    "def run_bad_ancestor_simulation(beta_ZX=1, beta_XY=1, n_simulations=500, unobserved_variance=None, n_samples_per_simualtion=100):\n",
    "    beta_X = beta_XY\n",
    "\n",
    "    simulations = np.array(\n",
    "        [\n",
    "            simulate_bad_ancestor(\n",
    "                beta_ZX=beta_ZX,\n",
    "                beta_XY=beta_XY,\n",
    "                unobserved_variance=unobserved_variance,\n",
    "                n_samples=n_samples_per_simualtion\n",
    "            ) \n",
    "            for _ in range(n_simulations)\n",
    "        ]\n",
    "    )\n",
    "    _, ax = plt.subplots(figsize=(8, 4))\n",
    "    az.plot_dist(simulations[:, 0], label='Y ~ X\\ncorrect', color='black', ax=ax)\n",
    "    az.plot_dist(simulations[:, 1], label='Y ~ X + Z\\nwrong', color=\"C0\", ax=ax)\n",
    "    plt.axvline(beta_X, color='k', linestyle='--', label=f'actual={beta_X}')\n",
    "    plt.legend(loc='upper left')\n",
    "    plt.xlabel(\"posterior mean\")\n",
    "    plt.ylabel(\"density\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "3f0e5ed9-53b2-45e3-995c-4183ab1d9706",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "run_bad_ancestor_simulation()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b49a25c4-cb39-4ed1-8797-f9e0fd090eda",
   "metadata": {},
   "source": [
    "Stratifying by Z doesn't add bias (it's centered on the correct value), but it does increase variance in estimator. This reduction in precision is proportional to the magnitude of the causal relationship between Z and X"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f70fc97e-4987-41de-a0ea-b34ee27eefc8",
   "metadata": {},
   "source": [
    "#### Increasing the relationshipe between Z and X further reduces precicision"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "549524a3-d2a6-453c-bc12-f0607734152d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "run_bad_ancestor_simulation(beta_ZX=3)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "78814ff3",
   "metadata": {},
   "source": [
    "## Bias Amplification\n",
    "\n",
    "Stratifying on an ancestor when there are other confounders, particularly unobserved forks. **This is like the Bias Parasite scenario, but it also adds bias.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "dd123012",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<title>unobserved</title>\n",
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       "<graphviz.graphs.Digraph at 0x11e0cb520>"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "utils.draw_causal_graph(\n",
    "    edge_list=[\n",
    "        (\"X\", \"Y\"),\n",
    "        (\"Z\", \"X\"),\n",
    "        (\"u\", \"X\"),\n",
    "        (\"u\", \"Y\")\n",
    "    ],\n",
    "    node_props={\n",
    "        \"X\": {\"color\": \"red\"},\n",
    "        \"Y\": {\"color\": \"red\"},\n",
    "        \"u\": {\"style\": \"dashed\"},\n",
    "        \"unobserved\": {\"style\": \"dashed\"},\n",
    "    },\n",
    "    edge_props={\n",
    "        (\"X\", \"Y\"): {\"color\": \"red\"},\n",
    "        (\"Y\", \"Z\"): {\"color\": \"red\"}\n",
    "    },\n",
    "    graph_direction=\"LR\"\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b0681fc5",
   "metadata": {},
   "source": [
    "### Verify via simulation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "bee895a8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/svg+xml": [
       "<?xml version=\"1.0\" encoding=\"UTF-8\" standalone=\"no\"?>\n",
       "<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n",
       " \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\n",
       "<!-- Generated by graphviz version 8.1.0 (0)\n",
       " -->\n",
       "<!-- Pages: 1 -->\n",
       "<svg width=\"308pt\" height=\"153pt\"\n",
       " viewBox=\"0.00 0.00 308.30 153.00\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n",
       "<g id=\"graph0\" class=\"graph\" transform=\"scale(1 1) rotate(0) translate(4 149)\">\n",
       "<polygon fill=\"white\" stroke=\"none\" points=\"-4,4 -4,-149 304.3,-149 304.3,4 -4,4\"/>\n",
       "<!-- X -->\n",
       "<g id=\"node1\" class=\"node\">\n",
       "<title>X</title>\n",
       "<ellipse fill=\"none\" stroke=\"red\" cx=\"176.55\" cy=\"-29\" rx=\"27\" ry=\"18\"/>\n",
       "<text text-anchor=\"middle\" x=\"176.55\" y=\"-23.95\" font-family=\"Times,serif\" font-size=\"14.00\">X</text>\n",
       "</g>\n",
       "<!-- Y -->\n",
       "<g id=\"node2\" class=\"node\">\n",
       "<title>Y</title>\n",
       "<ellipse fill=\"none\" stroke=\"red\" cx=\"273.3\" cy=\"-52\" rx=\"27\" ry=\"18\"/>\n",
       "<text text-anchor=\"middle\" x=\"273.3\" y=\"-46.95\" font-family=\"Times,serif\" font-size=\"14.00\">Y</text>\n",
       "</g>\n",
       "<!-- X&#45;&gt;Y -->\n",
       "<g id=\"edge1\" class=\"edge\">\n",
       "<title>X&#45;&gt;Y</title>\n",
       "<path fill=\"none\" stroke=\"red\" d=\"M202.44,-35.04C213,-37.6 225.49,-40.64 236.97,-43.42\"/>\n",
       "<polygon fill=\"red\" stroke=\"red\" points=\"236.03,-47.04 246.57,-46 237.68,-40.24 236.03,-47.04\"/>\n",
       "<text text-anchor=\"middle\" x=\"224.92\" y=\"-45.2\" font-family=\"Times,serif\" font-size=\"14.00\">0</text>\n",
       "</g>\n",
       "<!-- Z -->\n",
       "<g id=\"node3\" class=\"node\">\n",
       "<title>Z</title>\n",
       "<ellipse fill=\"none\" stroke=\"black\" cx=\"53.4\" cy=\"-18\" rx=\"27\" ry=\"18\"/>\n",
       "<text text-anchor=\"middle\" x=\"53.4\" y=\"-12.95\" font-family=\"Times,serif\" font-size=\"14.00\">Z</text>\n",
       "</g>\n",
       "<!-- Z&#45;&gt;X -->\n",
       "<g id=\"edge2\" class=\"edge\">\n",
       "<title>Z&#45;&gt;X</title>\n",
       "<path fill=\"none\" stroke=\"red\" d=\"M80.55,-17.26C95.53,-17.15 114.65,-17.53 131.55,-19.5 134.43,-19.84 137.41,-20.27 140.38,-20.78\"/>\n",
       "<polygon fill=\"red\" stroke=\"red\" points=\"139.54,-24.37 150.02,-22.83 140.86,-17.5 139.54,-24.37\"/>\n",
       "<text text-anchor=\"middle\" x=\"128.17\" y=\"-23.2\" font-family=\"Times,serif\" font-size=\"14.00\">1</text>\n",
       "</g>\n",
       "<!-- u -->\n",
       "<g id=\"node4\" class=\"node\">\n",
       "<title>u</title>\n",
       "<ellipse fill=\"none\" stroke=\"black\" stroke-dasharray=\"5,2\" cx=\"53.4\" cy=\"-73\" rx=\"27\" ry=\"18\"/>\n",
       "<text text-anchor=\"middle\" x=\"53.4\" y=\"-67.95\" font-family=\"Times,serif\" font-size=\"14.00\">u</text>\n",
       "</g>\n",
       "<!-- u&#45;&gt;X -->\n",
       "<g id=\"edge3\" class=\"edge\">\n",
       "<title>u&#45;&gt;X</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M77.76,-64.52C96.03,-57.88 121.67,-48.57 142.11,-41.14\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"143.06,-44.16 151.26,-37.46 140.67,-37.58 143.06,-44.16\"/>\n",
       "</g>\n",
       "<!-- u&#45;&gt;Y -->\n",
       "<g id=\"edge4\" class=\"edge\">\n",
       "<title>u&#45;&gt;Y</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M80.73,-72.27C114.89,-71.08 176.27,-68.08 228.3,-61 231,-60.63 233.79,-60.2 236.59,-59.72\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"237.08,-63 246.27,-57.72 235.79,-56.12 237.08,-63\"/>\n",
       "</g>\n",
       "<!-- unobserved -->\n",
       "<g id=\"node5\" class=\"node\">\n",
       "<title>unobserved</title>\n",
       "<ellipse fill=\"none\" stroke=\"black\" stroke-dasharray=\"5,2\" cx=\"53.4\" cy=\"-127\" rx=\"53.4\" ry=\"18\"/>\n",
       "<text text-anchor=\"middle\" x=\"53.4\" y=\"-121.95\" font-family=\"Times,serif\" font-size=\"14.00\">unobserved</text>\n",
       "</g>\n",
       "</g>\n",
       "</svg>\n"
      ],
      "text/plain": [
       "<graphviz.graphs.Digraph at 0x11e0cb8b0>"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "utils.draw_causal_graph(\n",
    "    edge_list=[\n",
    "        (\"X\", \"Y\"),\n",
    "        (\"Z\", \"X\"),\n",
    "        (\"u\", \"X\"),\n",
    "        (\"u\", \"Y\")\n",
    "    ],\n",
    "    node_props={\n",
    "        \"X\": {\"color\": \"red\"},\n",
    "        \"Y\": {\"color\": \"red\"},\n",
    "        \"u\": {\"style\": \"dashed\"},\n",
    "        \"unobserved\": {\"style\": \"dashed\"},\n",
    "    },\n",
    "    edge_props={\n",
    "        (\"X\", \"Y\"): {\"color\": \"red\", \"label\": \"0\"},\n",
    "        (\"Z\", \"X\"): {\"color\": \"red\", \"label\": \"1\"}\n",
    "    },\n",
    "    graph_direction=\"LR\"\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eb4309a2-fc3c-43d9-8222-09fd62923c3c",
   "metadata": {},
   "source": [
    "#### Run simulation with no actual causal effect, $\\beta_{XY} = 0$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "90aa5209-8026-4ee2-a02d-f37e64907662",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "run_bad_ancestor_simulation(beta_ZX=1, beta_XY=0, unobserved_variance=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "02d372b2",
   "metadata": {},
   "source": [
    "Above we see that both estimators are biased -- even in the best case, we can't observe, and thus control for the the confound $u$. But when stratifying by the ancestor, things are MUCH WORSE.\n",
    "\n",
    "Hand-wavy explanation:\n",
    "- in order for $X$ and $Y$ to be associated, their causes need to be associated\n",
    "- by stratifying by $Z$, we remove the amount of variation in $X$ that is caused by $Z$\n",
    "- this reduction in variation in $X$ makes the confound $u$ more important comparatively\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "df653d2d",
   "metadata": {},
   "source": [
    "### Disrete example of bias amplificiation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "fd86c4c4",
   "metadata": {},
   "outputs": [],
   "source": [
    "def simulate_discrete_bias_amplifications(beta_ZX=1, beta_XY=1, n_samples=1000):\n",
    "    Z = stats.bernoulli.rvs(p=0.5, size=n_samples)\n",
    "    u = stats.norm.rvs(size=n_samples)\n",
    "\n",
    "    mu_X = beta_ZX * Z + u\n",
    "    X = stats.norm.rvs(loc=mu_X, size=n_samples)\n",
    "\n",
    "    mu_Y = X * beta_XY + u\n",
    "    Y = stats.norm.rvs(loc=mu_Y, size=n_samples)\n",
    "\n",
    "    data = pd.DataFrame(np.vstack([u, Z, X, Y]).T, columns=['u', 'Z', 'X', 'Y'])\n",
    "\n",
    "    models = {}\n",
    "    models['unstratified'] = smf.ols(\"Y ~ X\", data=data).fit()\n",
    "    models['z=0'] = smf.ols(\"Y ~ X\", data=data[data.Z == 0]).fit()\n",
    "    models['z=1'] = smf.ols(\"Y ~ X\", data=data[data.Z == 1]).fit()\n",
    "    \n",
    "    return models, data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "e4b70b72",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "beta_ZX = 7\n",
    "beta_ZY = 0\n",
    "models, data = simulate_discrete_bias_amplifications(beta_ZX=beta_ZX, beta_XY=beta_XY)\n",
    "\n",
    "fig, axs = plt.subplots(figsize=(5, 5))\n",
    "\n",
    "def plot_linear_model(model, color, label):\n",
    "    params = model.params\n",
    "    xs = np.linspace(data.X.min(), data.X.max(), 10)\n",
    "    ys = params.Intercept + params.X * xs\n",
    "    utils.plot_line(xs, ys, color=color, label=label)\n",
    "    \n",
    "for z in [0, 1]:\n",
    "    color = f'C{z}'\n",
    "    utils.plot_scatter(data.X[data.Z==z], data.Y[data.Z==z], color=color)\n",
    "    model = models[f\"z={z}\"]\n",
    "    plot_linear_model(model, color=color, label=f\"Z={z}\")\n",
    "    \n",
    "model = models['unstratified']\n",
    "plot_linear_model(model, color=\"k\", label=f\"total sample\")\n",
    "\n",
    "plt.xlim([-5, 12])\n",
    "plt.ylim([-5, 5])\n",
    "\n",
    "plt.legend();\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a5b483dd",
   "metadata": {},
   "source": [
    "- When ignoring Z (the ancestor), the estimate is still somewhat biased (i.e. the black slope is not flat, as it shoudl be for $\\beta_{XY}=0$)\n",
    "- but it's not nearly as bad as the individual slopes (blue/red) when stratifying by Z. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "34ed2b55-0af1-41ca-a7fd-c121d0866005",
   "metadata": {},
   "source": [
    "# Review: Good & Bad Controls\n",
    "- **Confound**: estimator design or sample that \"confoudnds\" or \"confuses\" our causal estiamte\n",
    "- **Control**: variable added to the analysis to that a causal estimate is possible\n",
    "- Adding controls can often be worse than ommitting them\n",
    "- Make assumptions explicit, and use backdoor criterion to verify those assumptions\n",
    "\n",
    "**You have to do scientific modeling to do scientific analysis**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "665a9678",
   "metadata": {},
   "source": [
    "# BONUS: The Table 2 Fallacy\n",
    "- Not all coefficients represent causal effects, particularly those in the adjustment set\n",
    "- Those that _are_ causal effects tend to be partial effects, not total causal effects.\n",
    "- Table 2 actively _encourage_ misinterpretation\n",
    "- As mentioned multiple times: **Need different estimators for addressing different causal effects.**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9457007c",
   "metadata": {},
   "source": [
    "## Example: Smoking, Age, HIV, and Stroke"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "7794ff9c",
   "metadata": {},
   "outputs": [
    {
     "data": {
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     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "utils.draw_causal_graph(\n",
    "    edge_list=[\n",
    "        (\"X\", \"Y\"),\n",
    "        (\"S\", \"Y\"),\n",
    "        (\"S\", \"X\"),\n",
    "        (\"A\", \"Y\"),\n",
    "        (\"A\", \"X\"),\n",
    "        (\"A\", \"S\"),\n",
    "    ],\n",
    "    node_props={\n",
    "        \"X\": {\"label\": \"X, HIV\", \"color\": \"red\"},\n",
    "        \"Y\": {\"label\": \"Y, Stroke\", \"color\": \"red\"},\n",
    "        \"S\": {\"label\": \"S, Smoking\"},\n",
    "        \"A\": {\"label\": \"A, Age\"}\n",
    "    },\n",
    "    edge_props={\n",
    "        (\"X\", \"Y\"): {\"color\": \"red\"}\n",
    "    },\n",
    "    graph_direction=\"LR\"\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c1517e58",
   "metadata": {},
   "source": [
    "### Identify paths via backdoor criterion\n",
    "- $X \\rightarrow Y$ (front door)\n",
    "- $X \\leftarrow S \\rightarrow Y$ (backdoor, fork)\n",
    "- $X \\leftarrow A \\rightarrow Y$ (backdoor, fork)\n",
    "- $X \\leftarrow A \\rightarrow S \\rightarrow Y$ (backdoor, fork and pipe)\n",
    "\n",
    "Adjustment set is $\\{S, A\\}$\n",
    "\n",
    "### Conditional Statistical Model\n",
    "\n",
    "$$\n",
    "\\begin{align*}\n",
    "Y_i &\\sim \\mathcal{N}(\\mu_i, \\sigma) \\\\\n",
    "\\mu_i = &\\alpha + \\beta_X X_i + \\beta_S S_i + \\beta_A A_i\n",
    "\\end{align*}\n",
    "$$"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "89ac3a08",
   "metadata": {},
   "source": [
    "### Looking at the model \"from the perspective of various variables\"\n",
    "#### From perspective of X\n",
    "Conditioning on $A$ and $S$ essentially removes the arrows going into $X$, $\\beta_X$ giving us the **direct effect** of $X$ on $Y$\n",
    "  - we've removed all backdoor paths by stratifying by $S$ and $A$ throught the coefficients $\\beta_S, \\beta_A$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "b6e7d1d9",
   "metadata": {},
   "outputs": [
    {
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   "source": [
    "utils.draw_causal_graph(\n",
    "    edge_list=[\n",
    "        (\"X\", \"Y\"),\n",
    "        (\"S\", \"Y\"),\n",
    "        (\"S\", \"X\"),\n",
    "        (\"A\", \"Y\"),\n",
    "        (\"A\", \"X\"),\n",
    "        (\"A\", \"S\"),\n",
    "    ],\n",
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    "        \"X\": {\"color\": \"red\"},\n",
    "        \"Y\": {\"color\": \"red\"}\n",
    "    },\n",
    "    edge_props={\n",
    "        (\"X\", \"Y\"): {\"color\": \"red\"},\n",
    "        (\"A\", \"X\"): {\"color\": \"none\"},\n",
    "        (\"S\", \"X\"): {\"color\": \"none\"}\n",
    "    },\n",
    "    graph_direction=\"LR\"\n",
    ")"
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  },
  {
   "cell_type": "markdown",
   "id": "1bc86707",
   "metadata": {},
   "source": [
    "#### From perspective of S"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "7297f8cb",
   "metadata": {},
   "outputs": [
    {
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   "source": [
    "utils.draw_causal_graph(\n",
    "    edge_list=[\n",
    "        (\"X\", \"Y\"),\n",
    "        (\"S\", \"Y\"),\n",
    "        (\"S\", \"X\"),\n",
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    "        (\"S\", \"X\"): {\"color\": \"red\"},\n",
    "        (\"S\", \"Y\"): {\"color\": \"red\"}\n",
    "    },\n",
    "    graph_direction=\"LR\"\n",
    ")"
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  {
   "cell_type": "markdown",
   "id": "dc0281f0",
   "metadata": {},
   "source": [
    "##### Adjusted graph with the full model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "5822bf10",
   "metadata": {},
   "outputs": [
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    "utils.draw_causal_graph(\n",
    "    edge_list=[\n",
    "        (\"X\", \"Y\"),\n",
    "        (\"S\", \"Y\"),\n",
    "        (\"S\", \"X\"),\n",
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    "        (\"A\", \"X\"),\n",
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    "        \"S\": {\"color\": \"red\"}\n",
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    "        (\"A\", \"S\"): {\"color\": \"none\"},\n",
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    "- In the unconditional model, the effect of $S$ on $Y$ is confounded by $A$, because it's a common cause of $S$ and $Y$ (and $X$)\n",
    "- Conditioning on $A$ & $X$ (via the same statistical model above), $\\beta_{S}$ gives the **direct effect** of $S$ on $Y$/\n",
    "  - Since we've blocked the path along $X$ in the linear regression, we no longer get the **total effect**."
   ]
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   "cell_type": "markdown",
   "id": "dbb087cb",
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    "#### From the perspective of A\n",
    "\n",
    "In the unconditional model, the **total causal effect** of $A$ on $Y$ flows through all paths:"
   ]
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   "cell_type": "code",
   "execution_count": 51,
   "id": "4d867991",
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    "utils.draw_causal_graph(\n",
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    "        (\"S\", \"Y\"),\n",
    "        (\"S\", \"X\"),\n",
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    "- Conditioning on $S$ & $X$ (via the same statistical model above), $\\beta_{S}$ gives the **direct effect** of $S$ on $Y$/\n",
    "  - Since we've blocked the path along $X$ in the linear regression, we no longer get the **total effect**."
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   "cell_type": "code",
   "execution_count": 52,
   "id": "ad7c4e98",
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    "This gets trickier if we consider unobserved confounds on variables!\n",
    "\n",
    "## Summary: Table 2 Fallacy\n",
    "- Not all coefficients have the same interpretation\n",
    "  - different estimands require different models\n",
    "- Do not present coefficients as they are equal (i.e. in Table 2)\n",
    "- ...or, don't present coefficients at all, instead push out predictions.\n",
    "- Provide _explicit interpretation_ of each"
   ]
  }
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