{ "cells": [ { "cell_type": "code", "execution_count": 45, "metadata": {}, "outputs": [], "source": [ "import warnings\n", "\n", "import arviz as az\n", "import numpy as np\n", "import pandas as pd\n", "import pymc as pm\n", "import pymc_bart as pmb\n", "import pytensor.tensor as pt\n", "import statsmodels.formula.api as smf\n", "from matplotlib import pyplot as plt\n", "\n", "warnings.filterwarnings(\"ignore\", category=UserWarning)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Bayesian Structural Causal Inference\n", "\n", "When we ask \"What is the effect of a medical treatment?\" or \"Does quitting smoking cause weight gain?\" or \"Do job training programs increase earnings?\", we are not simply asking about the treatment itself. We are asking: What world are we operating in? This perspective is more easily seen if you imagine a causal analyst as a pet-shop owner introducing a new fish to one of their many acquariums. The new fish's survival and behavior depend less on its intrinsic properties than on how it fits within this complex, interconnected system of PH balances and predators. In which tank will the new fish thrive? \n", "\n", "Different causal methods make different choices about how much of this system to model explicitly. Some methods succeed by not modeling the full system: instrumental variables isolate causal effects through credible exclusion restrictions; difference-in-differences leverages parallel trends; interrupted time-series assumes stationarity. These design-based approaches gain power by minimizing modeling assumptions about the data-generating process. See {cite:t}`pearl2000causality` or {cite:t}`angrist2009mostly` for more detailed distinctions. The unifying thread between these diverse methods is the idea of a causal model as a _probabilistic program_ : an inferential routine designed to explicitly yield insights into the effect of some intervention or treatment on the system of interest. Whether design based or model-based, causal inference methods assume a data generating process - the distinction between these methods is how explicitly the system is rendered.\n", "\n", "#### Modelling Worlds and Counterfactual Worlds\n", "\n", "Bayesian structural modeling attempts to parameterize the system itself. Where design-based methods answer \"what is the causal effect under these identification assumptions?\", structural models ask \"what is the most plausible data-generating process, and how do interventions propagate through it?\". In Bayesian structural causal inference the focus is slightly different in that we wish to model both the treatment and the environment i.e. the fish and the fishtank. The trade-off is transparency for complexity. You must specify more of the data-generating process, which creates more opportunities for model misspecification. But every assumption becomes an explicit, testable model component rather than an implicit background condition.\n", "\n", "This is a two step move in the Bayesian paradigm. First we infer \"backwards\" what is the most plausible state of the world $w$ conditioned on the observable data. The \"world\" of the model is defined by: (1) a causal graph relating variables, (2) likelihood functions specifying how each variable depends on its causes, and (3) prior distributions over parameters. Optionally, this may include latent confounders, measurement models, and selection mechanisms—each adding structural detail but also complexity. With this world in place, we continue to assess the probabilistic predictive distribution of treatment and outcome at the plausible range of counterfactual worlds. \n", "\n", "![](../_static/forwards_backwards.png)\n", "\n", "The important point is that we characterise the plausible worlds by how much structure we learn about in the model specification. The more structure we seek to infer, the more we risk model misspecification, but simultaneously, the more structure we learn the more useful and transparent our conclusions. This structural commitment contrasts sharply with reduced-form approaches that minimize explicit modeling.\n", "\n", "#### Minimalism and Structural Maximalism\n", "\n", "The term \"reduced form\" originates from econometric simultaneous equations models. Early economists wanted to model supply and demand as functions of price, but faced a problem: quantities also determine price in competitive markets. Because these structural relationships are mutually determined, the system is hard to solve directly. The solution was algebraic transformation: solve for the 'reduced form' that expresses endogenous variables purely as functions of exogenous ones.\n", "\n", "Reduced form systems are transformed systems of interest designed to estimate the focal parameters by leveraging observable and tractable data. These approaches eschew \"theory driven\" model specifications in favour of models with precise _identifiable estimands_. This approach - transforming complex structural systems into tractable estimating equations - reflects a broader methodological commitment. It is for this minimalist preference that they are typically contrasted with structural models that aim to express the \"fuller\" data generating process. Design based causal inference methods typically adopt this focus on identifiability within a regression framework. For richer discussion in this vein see {cite:t}`hansenEconometrics` or {cite:t}`aronowFoundations`. \n", "\n", "When we regress an outcome $Y$ on a treatment $T$ and a set of covariates $X$,\n", "\n", "$$Y = \\alpha T + X \\beta + \\epsilon$$\n", "\n", "the coefficient $\\alpha$ captures the average change in Y associated with a one-unit change in $T$. Only under strong assumptions, however, can we interpret this as a causal effect. In real-world settings, those assumptions (like exogeneity of $T$) are fragile:\n", "\n", "- Confounding: Unobserved or omitted variables affect both \n", "$T$ and $Y$.\n", "\n", "- Endogeneity: Treatment assignment mechanisms are correlated with the error term.\n", "\n", "- Measurement uncertainty: Model parameters and predictions have uncertainty not captured by point estimates.\n", "\n", "The innovative methods of inference (like Two-stage least squares, propensity score weighting or DiD designs) that came to define the _credibility revolution_ in the social sciences, seek to overcome this risk of confounding with constraints or assumptions to bolster identification of the causal parameters. See See {cite:t}`angrist2009mostly`. Bayesian probabilistic causal inference addresses these challenges by explicitly modelling the data-generating process and quantifying all sources of uncertainty. Rather than point estimates and design assumptions, we infer full posterior distributions over causal parameters and even over counterfactual outcomes. Rather than isolating the outcome equation from the treatment equation, we model them together as parts of a single generative system. This approach mirrors how interventions occur in the real world. The propensity for adopting a treatment can be predicted by the same factors which determine treatment outcomes. This structure creates the risk of confounding because the efficacy of the treatment is obscured by the influence of these shared predictors. When we fit such a model, we learn about every component simultaneously—the effect of the treatment, the influence of confounders, and the uncertainty that ties them together. Once fitted, Bayesian models can generate posterior predictive draws for “what if” scenarios. This capacity lets us compute causal estimands like the ATE or individual treatment effects directly from the posterior.\n", "\n", "In this tutorial, we’ll move step by step from data simulation to Structural Bayesian Causal models:\n", "\n", ":::{admonition} The Structure of the Document\n", ":class: tip\n", "\n", "- Simulate data with known causal structure (including confounding and exclusion restrictions).\n", "\n", "- Fit and interpret Bayesian models for continuous treatments.\n", "\n", "- Extend to binary treatments and potential outcomes.\n", "\n", "- Use posterior predictive imputation to simulate counterfactuals.\n", "\n", "- Demonstrate the relationship between the structural modelling perspective with the potential outcomes framework.\n", "\n", "- Apply the model to an empircal example with parameter recovery checks and sensitivity analysis\n", ":::\n", "\n", "\n", "This approach will show how Bayesian methods provide a unified and transparent lens on causal inference. We will cover estimation, identification, and uncertainty in a single coherent framework.\n", "\n", "### Simulating the Source of Truth\n", "\n", "Every causal claim rests on untestable assumptions about the data-generating process. Before we can trust our methods in the wild, we must test them in controlled conditions where truth is known. The simulation below constructs such a laboratory: we specify the causal structure explicitly, introduce confounding deliberately, and then ask whether our Bayesian models recover what we seeded in the data. " ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "np.random.seed(123)\n", "\n", "\n", "def inv_logit(z):\n", " \"\"\"Compute the inverse logit (sigmoid) of z.\"\"\"\n", " return 1 / (1 + np.exp(-z))\n", "\n", "\n", "def standardize_df(df, cols):\n", " means = df[cols].mean()\n", " sds = df[cols].std(ddof=1)\n", " df_s = (df[cols] - means) / sds\n", " return df_s, means, sds\n", "\n", "\n", "def simulate_data(n=2500, alpha_true=3.0, rho=0.6, cate_estimation=False):\n", " # Exclusion restrictions:\n", " # X[0], X[1] affect both Y and T (confounders)\n", " # X[2], X[3] affect ONLY T (instruments for T)\n", " # X[4] affects ONLY Y (predictor of Y only)\n", "\n", " betaY = np.array([0.5, -0.3, 0.0, 0.0, 0.4, 0, 0, 0, 0]) # X[2], X[3] excluded\n", " betaD = np.array([0.7, 0.1, -0.4, 0.3, 0.0, 0, 0, 0, 0]) # X[4] excluded\n", " p = len(betaY)\n", "\n", " # noise variances and correlation\n", " sigma_U = 3.0\n", " sigma_V = 3.0\n", "\n", " # design matrix (n × p) with mean-zero columns\n", " X = np.random.normal(size=(n, p))\n", " X = (X - X.mean(axis=0)) / X.std(axis=0)\n", "\n", " mean = [0, 0]\n", " cov = [[sigma_U**2, rho * sigma_U * sigma_V], [rho * sigma_U * sigma_V, sigma_V**2]]\n", " errors = np.random.multivariate_normal(mean, cov, size=n)\n", " U = errors[:, 0] # error in outcome equation\n", " V = errors[:, 1] #\n", "\n", " # continuous treatment\n", " T_cont = X @ betaD + V\n", "\n", " # latent variable for binary treatment\n", " T_latent = X @ betaD + V\n", " T_bin = np.random.binomial(n=1, p=inv_logit(T_latent), size=n)\n", "\n", " alpha_individual = 3.0 + 2.5 * X[:, 0]\n", "\n", " # outcomes\n", " Y_cont = alpha_true * T_cont + X @ betaY + U\n", " if cate_estimation:\n", " Y_bin = alpha_individual * T_bin + X @ betaY + U\n", " else:\n", " Y_bin = alpha_true * T_bin + X @ betaY + U\n", "\n", " # combine into DataFrame\n", " data = pd.DataFrame(\n", " {\n", " \"Y_cont\": Y_cont,\n", " \"Y_bin\": Y_bin,\n", " \"T_cont\": T_cont,\n", " \"T_bin\": T_bin,\n", " }\n", " )\n", " data[\"alpha\"] = alpha_true + alpha_individual\n", " for j in range(p):\n", " data[f\"feature_{j}\"] = X[:, j]\n", " data[\"Y_cont_scaled\"] = (data[\"Y_cont\"] - data[\"Y_cont\"].mean()) / data[\n", " \"Y_cont\"\n", " ].std(ddof=1)\n", " data[\"Y_bin_scaled\"] = (data[\"Y_bin\"] - data[\"Y_bin\"].mean()) / data[\"Y_bin\"].std(\n", " ddof=1\n", " )\n", " data[\"T_cont_scaled\"] = (data[\"T_cont\"] - data[\"T_cont\"].mean()) / data[\n", " \"T_cont\"\n", " ].std(ddof=1)\n", " data[\"T_bin_scaled\"] = (data[\"T_bin\"] - data[\"T_bin\"].mean()) / data[\"T_bin\"].std(\n", " ddof=1\n", " )\n", " return data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Each simulated observation has a treatment $T$, an outcome $Y$, and a set of covariates $X$ with distinct causal roles. Two covariates influence both the treatment and the outcome—these are the confounders. Two others affect only the treatment and serve as valid instruments. A final covariate affects only the outcome. The treatment and outcome errors are drawn from a correlated bivariate normal distribution, introducing endogeneity through their correlation parameter $\\rho$. When $\\rho$ is low the treatment can be considered exogenous and standard regression should recover the correct effect; while naive estimates will be biased when $\\rho$ is high.\n", "\n", "#### Confounding Structure\n", "\n", "The function produces both continuous and binary versions of the treatment and the outcome. This dual design lets us explore two worlds side by side: one where the treatment is a continuous dosage, and another where it is a binary decision. In both cases, the true causal effect of the treatment on the outcome is set to three. Because we know the truth, we can evaluate how well our Bayesian models recover true parameters. Even here you can see that the \"structure\" we impose on the world is abstraction over the concrete mechanisms acting in the world. We bundle the idea of selecting into the treatment as potential for correlation between treatment and outcome. This is a convenient and tractable proxy of a range of concrete settings where there is a risk of selection effects in the real world. \n", "\n", "![](../_static/JOINT_DAG.png)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In the simulation code and the diagram above we have allowed the treatment and outcome to be predicted by shared variables `X0` and `X1`. These alone are sufficient to induce confounding into the estimation of the treatment on the outcome. We have also allowed `X2`, `X3` are potentially viable instrumental variables for predicting the outcome purged of the confounding effects of `X0` and `X1`. The rest of the variables are either noise or an independent predictor of the outcome. \n", "\n", "Before introducing the Bayesian machinery, it’s worth revisiting what goes wrong with ordinary least squares when the treatment and outcome share unobserved causes. The following code performs a simple sensitivity experiment: we vary the correlation $\\rho$ between the unobserved treatment and outcome errors and examine how the estimated treatment effect changes." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ " treatment_effects_binary treatment_effects_continuous rho\n", "0 -1.201719 2.000000 -1.000000\n", "1 -0.351161 2.221169 -0.777778\n", "2 0.766409 2.457946 -0.555556\n", "3 1.644626 2.652292 -0.333333\n", "4 2.712099 2.924260 -0.111111\n", "5 3.441161 3.117569 0.111111\n", "6 4.347816 3.327324 0.333333\n", "7 5.243494 3.537410 0.555556\n", "8 6.258062 3.782707 0.777778\n", "9 7.248784 4.000000 1.000000" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data = simulate_data(n=2500, alpha_true=3, rho=0.6)\n", "features = [col for col in data.columns if \"feature\" in col]\n", "\n", "treatment_effects_binary = []\n", "treatment_effects_continuous = []\n", "df_params = {\n", " \"treatment_effects_binary\": [],\n", " \"treatment_effects_continuous\": [],\n", " \"rho\": [],\n", "}\n", "formula_cont = \"Y_cont ~ T_cont + \" + \" + \".join(features)\n", "formula_bin = \"Y_bin ~ T_bin + \" + \" + \".join(features)\n", "for rho in np.linspace(-1, 1, 10):\n", " data = simulate_data(n=2500, alpha_true=3, rho=rho)\n", " model_cont = smf.ols(formula_cont, data=data).fit()\n", " model_bin = smf.ols(formula_bin, data=data).fit()\n", " df_params[\"treatment_effects_continuous\"].append(model_cont.params[\"T_cont\"])\n", " df_params[\"treatment_effects_binary\"].append(model_bin.params[\"T_bin\"])\n", " df_params[\"rho\"].append(rho)\n", "\n", "df_params = pd.DataFrame(df_params)\n", "df_params" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This loop re-simulates the dataset ten times, each with a different value of $\\rho$, ranging from –1 to 1. For each dataset, it fits two OLS regressions: one for the continuous treatment, and another for the binary treatment, both controlling for all observed covariates. The estimated coefficient on the treatment variable `T_cont` or `T_bin`—represents what OLS believes to be the causal effect. By collecting these estimates in df_params, we can plot them against the true correlation to see how endogeneity distorts inference.\n", "\n", "When $\\rho = 0$ the treatment and outcome errors are independent, and OLS recovers the true causal effect of 3. But as $\\rho$ grows, the estimates drift away from the truth, sometimes dramatically. The direction of bias depends on the sign of the unobserved relationship. If hidden factors push both treatment and outcome the same way, OLS overstates the effect. If they act in opposite directions, it understates it. Even though we’ve controlled for all observed features, the unobserved correlation sneaks bias into our estimates." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "image/png": 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots(figsize=(20, 6))\n", "ax.plot(\n", " df_params[\"rho\"], df_params[\"treatment_effects_binary\"], label=\"Binary Treatment\"\n", ")\n", "ax.plot(\n", " df_params[\"rho\"],\n", " df_params[\"treatment_effects_continuous\"],\n", " label=\"Continuous Treatment\",\n", ")\n", "ax.axhline(3, linestyle=\"--\", color=\"k\", label=\"True Treatment Effect\")\n", "ax.set_xlabel(\"rho \\n correlation parameter\")\n", "ax.set_ylabel(\"Treatment Effect Estimate\")\n", "ax.set_title(\"Treatment Effect Estimates with OLS \\n Under Confounding\")\n", "ax.legend();" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We now move from diagnosing bias to building a model that can recover causal effects under controlled conditions. To keep things interpretable, we begin with the unconfounded case, where the treatment and outcome share no latent correlation ($\\rho=0$). This setting lets us isolate what a Bayesian structural model actually does before we expose it to the challenges of endogeneity.\n", "\n", "#### Joint Modelling and Prior Structure\n", "\n", "At the heart of our approach is joint modelling: instead of fitting separate regressions for treatment and outcome, we model them together as draws from a joint multivariate distribution. The treatment equation captures how covariates predict exposure, while the outcome equation captures how both treatment and covariates predict the response. By expressing them jointly, we retain the covariance structure between their errors—an essential ingredient for causal inference once we later introduce confounding.\n", "\n", "The model is built using PyMC and organized through the function `make_joint_model()`. Each version shares the same generative logic but differs in how the priors handle variable selection and identification. We can think of these as different “dial settings” for how strongly the model shrinks irrelevant coefficients or searches for valid instruments. Four prior configurations are explored:\n", "\n", "- A normal prior, weak regularization with no variable selection. If the model succeeds here, the causal structure is identified through the joint modeling alone.\n", "\n", "- A spike-and-slab prior, which aggressively prunes away variables unlikely to matter, allowing the model to discover which features are true confounders or instruments.\n", "\n", "- A horseshoe prior, offering continuous shrinkage that downweights noise while preserving large signals. This is a middle path that downweights weak predictors without forcing them exactly to zero.\n", "\n", "- An exclusion-restriction prior, explicitly encoding which variables are allowed to influence the treatment but not the outcome, mimicking an instrumental-variable design.\n", "\n", "Each prior embodies a different epistemological stance on how much structure the data can learn versus how much the analyst must impose. In the unconfounded case, the treatment and outcome errors are independent, so the joint model effectively decomposes into two connected regressions. The treatment effect $\\alpha$ then captures the causal impact of the treatment on the outcome, and under this setting, its posterior should center around the true value of 3. The goal is not to solve confounding yet but to show that when the world is simple and well-behaved, the Bayesian model recovers the truth just as OLS does—but with richer uncertainty quantification and a coherent probabilistic structure.\n", "\n", "The following code defines the model and instantiates it under several prior choices. The model’s graphical representation, produced by `pm.model_to_graphviz()`, visualizes its structure: covariates feed into both the treatment and the outcome equations, the treatment coefficient $\\alpha$ links them, and the two residuals \n", "$U$ and $V$ are connected through a correlation parameter $\\rho$, which we can freely set to zero or more substantive values. These parameterisations offer us a way to derive insight from the structure of the causal system under study. \n", "\n", "### Fitting the Continuous Treatment Model\n", "\n", "In this next code block we articulate the joint model for the continuous outcome and continuous treatment variable. " ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "image/svg+xml": [ "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "clusterbeta_outcome (9)\n", "\n", "beta_outcome (9)\n", "\n", "\n", "cluster9\n", "\n", "9\n", "\n", "\n", "clusterbeta_treatment (9)\n", "\n", "beta_treatment (9)\n", "\n", "\n", "cluster2500 x 9\n", "\n", "2500 x 9\n", "\n", "\n", "cluster2500 x 2\n", "\n", "2500 x 2\n", "\n", "\n", "cluster2500\n", "\n", "2500\n", "\n", "\n", "\n", "pi_T\n", "\n", "pi_T\n", "~\n", "Beta\n", "\n", "\n", "\n", "gamma_T\n", "\n", "gamma_T\n", "~\n", "Deterministic\n", "\n", "\n", "\n", "pi_T->gamma_T\n", "\n", "\n", "\n", "\n", "\n", "upper\n", "\n", "upper\n", "~\n", "Deterministic\n", "\n", "\n", "\n", "rho\n", "\n", "rho\n", "~\n", "Uniform\n", "\n", "\n", "\n", "upper->rho\n", "\n", "\n", "\n", "\n", "\n", "m\n", "\n", "m\n", "~\n", "Truncated_normal\n", "\n", "\n", "\n", "m->upper\n", "\n", "\n", "\n", "\n", "\n", "lower\n", "\n", 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\"sigmas_2\": [\"cov_VU\", \"var_V\"],\n", "}\n", "\n", "\n", "def relaxed_bernoulli(name, p, temperature=0.1, dims=None):\n", " u = pm.Uniform(name + \"_u\", 0, 1, dims=dims)\n", " logit_p = pt.log(p) - pt.log(1 - p)\n", " return pm.Deterministic(\n", " name, pm.math.sigmoid((logit_p + pt.log(u) - pt.log(1 - u)) / temperature)\n", " )\n", "\n", "\n", "def make_joint_model(X, Y, T, coords, priors_type=\"normal\", priors={}):\n", " p = X.shape[1]\n", " p0 = 5.0 # pick an expected number of nonzero coeffs\n", " sigma_est = 1.0\n", "\n", " tau0 = (p0 / (p - p0)) * (sigma_est / np.sqrt(X.shape[0]))\n", "\n", " with pm.Model(coords=coords) as dml_model:\n", " spike_and_slab = priors_type == \"spike_and_slab\"\n", " horseshoe = priors_type == \"horseshoe\"\n", " exclusion_restriction = priors_type == \"exclusion_restriction\"\n", " p = X.shape[1]\n", "\n", " if not priors:\n", " priors = {\n", " \"rho\": [-0.99, 0.99],\n", " }\n", "\n", " if spike_and_slab:\n", " # RELAXED SPIKE-AND-SLAB PRIORS for aggressive variable selection\n", "\n", " pi_O = pm.Beta(\"pi_O\", alpha=2, beta=2)\n", " beta_O_raw = pm.Normal(\"beta_O_raw\", mu=0, sigma=2, dims=\"beta_outcome\")\n", " gamma_O = relaxed_bernoulli(\n", " \"gamma_O\", pi_O, temperature=0.1, dims=\"beta_outcome\"\n", " )\n", " beta_outcome = pm.Deterministic(\n", " \"beta_O\", gamma_O * beta_O_raw, dims=\"beta_outcome\"\n", " )\n", "\n", " pi_T = pm.Beta(\"pi_T\", alpha=2, beta=2)\n", " beta_T_raw = pm.Normal(\"beta_T_raw\", mu=0, sigma=2, dims=\"beta_treatment\")\n", " gamma_T = relaxed_bernoulli(\n", " \"gamma_T\", pi_T, temperature=0.1, dims=\"beta_treatment\"\n", " )\n", " beta_treatment = pm.Deterministic(\n", " \"beta_T\", gamma_T * beta_T_raw, dims=\"beta_treatment\"\n", " )\n", "\n", " elif horseshoe:\n", " tau_O = pm.HalfStudentT(\"tau_O\", nu=3, sigma=tau0)\n", " # Local shrinkage parameters (one per coefficient)\n", " lambda_O = pm.HalfCauchy(\"lambda_O\", beta=1.0, dims=\"beta_outcome\")\n", " # Regularized horseshoe: c² controls tail behavior\n", " c2_O = pm.InverseGamma(\"c2_O\", alpha=2, beta=2)\n", " lambda_tilde_O = pm.Deterministic(\n", " \"lambda_tilde_O\",\n", " pm.math.sqrt(c2_O * lambda_O**2 / (c2_O + tau_O**2 * lambda_O**2)),\n", " dims=\"beta_outcome\",\n", " )\n", "\n", " # Outcome coefficients with horseshoe prior\n", " beta_O_raw = pm.Normal(\"beta_O_raw\", mu=0, sigma=1, dims=\"beta_outcome\")\n", " beta_outcome = pm.Deterministic(\n", " \"beta_O\", beta_O_raw * lambda_tilde_O * tau_O, dims=\"beta_outcome\"\n", " )\n", "\n", " # Same for treatment equation\n", " tau_T = pm.HalfStudentT(\"tau_T\", nu=3, sigma=tau0)\n", " lambda_T = pm.HalfCauchy(\"lambda_T\", beta=1.0, dims=\"beta_treatment\")\n", " c2_T = pm.InverseGamma(\"c2_T\", alpha=2, beta=2)\n", " lambda_tilde_T = pm.Deterministic(\n", " \"lambda_tilde_T\",\n", " pm.math.sqrt(c2_T * lambda_T**2 / (c2_T + tau_T**2 * lambda_T**2)),\n", " dims=\"beta_treatment\",\n", " )\n", "\n", " beta_T_raw = pm.Normal(\"beta_T_raw\", mu=0, sigma=1, dims=\"beta_treatment\")\n", " beta_treatment = pm.Deterministic(\n", " \"beta_T\", beta_T_raw * lambda_tilde_T * tau_T, dims=\"beta_treatment\"\n", " )\n", " elif exclusion_restriction:\n", " ### Ensuring that there is an instruments i.e. predictors of the treatment that\n", " ### impact the outcome only through the treatment\n", " beta_outcome = pm.Normal(\n", " \"beta_O\",\n", " 0,\n", " [2.0, 2.0, 0.001, 0.001, 2.0, 2, 2, 2, 2],\n", " dims=\"beta_outcome\",\n", " )\n", " beta_treatment = pm.Normal(\n", " \"beta_T\",\n", " 0,\n", " [2.0, 2.0, 2.0, 2.0, 0.001, 2, 2, 2, 2],\n", " dims=\"beta_treatment\",\n", " )\n", " else:\n", " beta_outcome = pm.Normal(\"beta_O\", 0, 1, dims=\"beta_outcome\")\n", " beta_treatment = pm.Normal(\"beta_T\", 0, 1, dims=\"beta_treatment\")\n", "\n", " X_data = pm.Data(\"X_data\", X.values)\n", " observed_data = pm.Data(\"observed\", np.column_stack([Y.values, T.values]))\n", "\n", " alpha = pm.Normal(\"alpha\", mu=0, sigma=5)\n", "\n", " # Error standard deviations\n", " sigma_U = pm.Exponential(\"sigma_U\", 1.0)\n", " sigma_V = pm.Exponential(\"sigma_V\", 1.0)\n", "\n", " # Correlation between errors (confounding parameter)\n", " m = pm.TruncatedNormal(\n", " \"m\", mu=0, sigma=0.5, lower=priors[\"rho\"][0], upper=priors[\"rho\"][1]\n", " )\n", " s = pm.Beta(\"s\", 2, 2) # scaled half-width\n", " h = pm.Deterministic(\"h\", s * (priors[\"rho\"][1] - pm.math.abs(m)))\n", " lower = pm.Deterministic(\"lower\", m - h)\n", " upper = pm.Deterministic(\"upper\", m + h)\n", " rho = pm.Uniform(\"rho\", lower, upper)\n", "\n", " mu_treatment = pm.Deterministic(\"mu_treatment\", X_data @ beta_treatment)\n", " mu_outcome = pm.Deterministic(\n", " \"mu_outcome\", X_data @ beta_outcome + alpha * mu_treatment\n", " )\n", "\n", " var_D = sigma_V**2\n", " var_Y = alpha**2 * sigma_V**2 + sigma_U**2 + 2 * alpha * rho * sigma_U * sigma_V\n", " cov_YD = alpha * sigma_V**2 + rho * sigma_U * sigma_V\n", "\n", " # Build 2x2 covariance matrix\n", " cov = pm.math.stack([[var_Y, cov_YD], [cov_YD, var_D]])\n", "\n", " # Store as deterministic for inspection\n", " _ = pm.Deterministic(\"var_Y\", var_Y)\n", " _ = pm.Deterministic(\"var_D\", var_D)\n", " _ = pm.Deterministic(\"cov_YD\", cov_YD)\n", " _ = pm.Deterministic(\"cov_UV\", rho * sigma_U * sigma_V)\n", "\n", " mu = pm.math.stack([mu_outcome, mu_treatment], axis=1) # shape (n,2)\n", " _ = pm.MvNormal(\"likelihood\", mu=mu, cov=cov, observed=observed_data)\n", "\n", " return dml_model\n", "\n", "\n", "def make_continuous_models(data):\n", " X = data[[col for col in data.columns if \"feature\" in col]]\n", " Y = data[\"Y_cont\"]\n", " T = data[\"T_cont\"]\n", "\n", " coords = {\n", " \"beta_outcome\": [col for col in data.columns if \"feature\" in col],\n", " \"beta_treatment\": [col for col in data.columns if \"feature\" in col],\n", " \"obs\": range(data.shape[0]),\n", " }\n", "\n", " spike_and_slab = make_joint_model(X, Y, T, coords, priors_type=\"spike_and_slab\")\n", " horseshoe = make_joint_model(X, Y, T, coords, priors_type=\"horseshoe\")\n", " excl = make_joint_model(X, Y, T, coords, priors_type=\"exclusion_restriction\")\n", " normal = make_joint_model(X, Y, T, coords, priors_type=\"normal\")\n", " tight_rho = make_joint_model(\n", " X, Y, T, coords, priors_type=\"normal\", priors={\"rho\": [0.4, 0.99]}\n", " )\n", " tight_rho_s_s = make_joint_model(\n", " X, Y, T, coords, priors_type=\"spike_and_slab\", priors={\"rho\": [0.4, 0.99]}\n", " )\n", "\n", " models = {\n", " \"spike_and_slab\": spike_and_slab,\n", " \"horseshoe\": horseshoe,\n", " \"exclusion\": excl,\n", " \"normal\": normal,\n", " \"tight_rho\": tight_rho,\n", " \"tight_rho_s_s\": tight_rho_s_s,\n", " }\n", " return models\n", "\n", "\n", "data_confounded = simulate_data(n=2500, alpha_true=3, rho=0.6)\n", "data_unconfounded = simulate_data(n=2500, alpha_true=3, rho=0)\n", "\n", "models_confounded = make_continuous_models(data_confounded)\n", "models_unconfounded = make_continuous_models(data_unconfounded)\n", "\n", "pm.model_to_graphviz(models_confounded[\"spike_and_slab\"])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This section orchestrates the fitting and sampling workflow for the suite of Bayesian models defined earlier. Having specified several variants of the joint outcome–treatment model—each differing only in its prior structure or treatment of the correlation parameter $\\rho$—we now turn to posterior inference.\n", "\n", "#### Various Model Specifications\n", "\n", "The functions `sample_model()`, and `fit_models()` provide a compact, repeatable sampling pipeline. Within the model context, it first draws from the prior predictive distribution, capturing what the model believes about the data before seeing any observations. These are comparable across each of models specified.\n", "We're moving from describing how the data are assumed to arise, to actually learning from the simulated observations. This is the backwards inference step. The output `idata_unconfounded` contains all posterior draws, prior predictive samples, and posterior predictive simulations for every model variant under the assumption of no confounding. This will allow us to compare the inferences achieved under each setting. To gauge which are the most plausible parameterisations of the world-state conditioned on the data and our model-specification." ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "tags": [ "hide-output" ] }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Sampling: [alpha, beta_O_raw, beta_T_raw, gamma_O_u, gamma_T_u, likelihood, m, pi_O, pi_T, rho, s, sigma_U, sigma_V]\n", "Initializing NUTS using jitter+adapt_diag...\n", "Multiprocess sampling (4 chains in 4 jobs)\n", "NUTS: [pi_O, beta_O_raw, gamma_O_u, pi_T, beta_T_raw, gamma_T_u, alpha, sigma_U, sigma_V, m, s, rho]\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "51ec55e391874c0391e316c13fd582e1", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Output()" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
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       "Output()"
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    {
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     "output_type": "stream",
     "text": [
      "Sampling 4 chains for 2_000 tune and 1_000 draw iterations (8_000 + 4_000 draws total) took 120 seconds.\n",
      "There were 2 divergences after tuning. Increase `target_accept` or reparameterize.\n",
      "Sampling: [likelihood]\n"
     ]
    },
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     "text": [
      "Sampling: [alpha, beta_O, beta_T, likelihood, m, rho, s, sigma_U, sigma_V]\n",
      "Initializing NUTS using jitter+adapt_diag...\n",
      "Multiprocess sampling (4 chains in 4 jobs)\n",
      "NUTS: [beta_O, beta_T, alpha, sigma_U, sigma_V, m, s, rho]\n"
     ]
    },
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     "output_type": "stream",
     "text": [
      "Sampling 4 chains for 2_000 tune and 1_000 draw iterations (8_000 + 4_000 draws total) took 90 seconds.\n",
      "Sampling: [likelihood]\n"
     ]
    },
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     "output_type": "stream",
     "text": [
      "Sampling: [alpha, beta_O_raw, beta_T_raw, gamma_O_u, gamma_T_u, likelihood, m, pi_O, pi_T, rho, s, sigma_U, sigma_V]\n",
      "Initializing NUTS using jitter+adapt_diag...\n",
      "Multiprocess sampling (4 chains in 4 jobs)\n",
      "NUTS: [pi_O, beta_O_raw, gamma_O_u, pi_T, beta_T_raw, gamma_T_u, alpha, sigma_U, sigma_V, m, s, rho]\n"
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     "output_type": "stream",
     "text": [
      "Sampling 4 chains for 2_000 tune and 1_000 draw iterations (8_000 + 4_000 draws total) took 387 seconds.\n",
      "Sampling: [likelihood]\n"
     ]
    },
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   ],
   "source": [
    "def sample_model(model, fit_kwargs):\n",
    "    with model:\n",
    "        idata = pm.sample_prior_predictive()\n",
    "        idata.extend(\n",
    "            pm.sample(\n",
    "                draws=1000,\n",
    "                tune=2000,\n",
    "                target_accept=0.95,\n",
    "                **fit_kwargs,\n",
    "                idata_kwargs={\"log_likelihood\": True},\n",
    "            )\n",
    "        )\n",
    "        idata.extend(pm.sample_posterior_predictive(idata))\n",
    "    return idata\n",
    "\n",
    "\n",
    "fit_kwargs = {}\n",
    "\n",
    "\n",
    "def fit_models(fit_kwargs, models):\n",
    "    idata_spike_and_slab = sample_model(models[\"spike_and_slab\"], fit_kwargs=fit_kwargs)\n",
    "    idata_horseshoe = sample_model(models[\"horseshoe\"], fit_kwargs=fit_kwargs)\n",
    "    idata_excl = sample_model(models[\"exclusion\"], fit_kwargs=fit_kwargs)\n",
    "    idata_normal = sample_model(models[\"normal\"], fit_kwargs=fit_kwargs)\n",
    "    idata_normal_rho_tight = sample_model(models[\"tight_rho\"], fit_kwargs=fit_kwargs)\n",
    "    idata_rho_tight_s_s = sample_model(models[\"tight_rho_s_s\"], fit_kwargs=fit_kwargs)\n",
    "\n",
    "    idatas = {\n",
    "        \"spike_and_slab\": idata_spike_and_slab,\n",
    "        \"horseshoe\": idata_horseshoe,\n",
    "        \"exclusion\": idata_excl,\n",
    "        \"normal\": idata_normal,\n",
    "        \"rho_tight\": idata_normal_rho_tight,\n",
    "        \"rho_tight_spike_slab\": idata_rho_tight_s_s,\n",
    "    }\n",
    "\n",
    "    return idatas\n",
    "\n",
    "\n",
    "idata_unconfounded = fit_models(fit_kwargs, models_unconfounded)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Before examining how different priors shape inference, it’s useful to clarify what our models are actually estimating. Each specification—spike-and-slab, horseshoe, exclusion restriction, and the others—ultimately targets the same estimand: the slope $\\alpha$ that captures how changes in the continuous treatment $T$ shift the expected outcome $Y$. In this setup, $\\alpha$ functions as a regression coefficient within the structural equation of our joint model. \n",
    "\n",
    "In econometric terms, what we’ve done so far sits squarely within the structural modelling tradition. We’ve written down a joint model for both the treatment and the outcome, specified their stochastic dependencies explicitly, and interpreted the slope $\\alpha$ as a structural parameter — a feature of the data-generating process itself. This parameter has a causal meaning only insofar as the model is correctly specified: if the structural form reflects how the world actually works, \n",
    "$\\alpha$ recovers the true causal effect. By contrast, reduced-form econometrics focuses less on modelling the underlying mechanisms and more on identifying causal effects through observable associations research design — instrumental variables, difference-in-differences, or randomization. Reduced-form approaches avoid the need to specify the joint distribution of unobservables but often sacrifice interpretability: they estimate relationships that are valid for specific interventions or designs, not necessarily structural primitives.\n",
    "\n",
    "#### Comparing Treatment Estimates\n",
    "\n",
    "The comparison of models is a form of robustness checks. We want to inspect how consistent our parameter estimates are across different model specifications. Here we see how the strongly informative priors on $\\rho$ bias the treatment effect estimate.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [
    {
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      "text/plain": [
       "
"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = az.plot_forest(\n",
    "    [\n",
    "        idata_unconfounded[\"spike_and_slab\"],\n",
    "        idata_unconfounded[\"horseshoe\"],\n",
    "        idata_unconfounded[\"exclusion\"],\n",
    "        idata_unconfounded[\"normal\"],\n",
    "        idata_unconfounded[\"rho_tight\"],\n",
    "        idata_unconfounded[\"rho_tight_spike_slab\"],\n",
    "    ],\n",
    "    var_names=[\"alpha\", \"rho\", \"beta_O\", \"beta_T\"],\n",
    "    combined=True,\n",
    "    model_names=[\n",
    "        \"spike_slab\",\n",
    "        \"horse shoe\",\n",
    "        \"exclusion_restriction\",\n",
    "        \"normal\",\n",
    "        \"tight_rho\",\n",
    "        \"tight_rho_spike_slab\",\n",
    "    ],\n",
    "    figsize=(20, 15),\n",
    ")\n",
    "\n",
    "ax[0].axvline(3, linestyle=\"--\", color=\"k\")\n",
    "ax[0].set_title(\n",
    "    \"Comparing Parameter Estimates across Model Specifications\", fontsize=15\n",
    ");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the plot we can see that the majority of models accurately estimate the true treatment effect $\\alpha$ except in the cases where we have explicitly placed an opinionated prior on the $\\rho$ parameter in the model. These priors pull the $\\alpha$ estimate away from the true data generating process. The variable selection priors considerably shrink the uncertainty in the treatment estimates seemingly picking out the implicit instrument structure aping the application of instrumental variables. \n",
    "\n",
    "Our Bayesian setup here is intentionally structural. We specify how both treatment and outcome arise from common covariates and latent confounding structures. However, the boundary between structural and reduced-form reasoning becomes fluid when we begin to treat latent variables or exclusion restrictions as data-driven “instruments.” In that sense, the structural Bayesian approach can emulate reduced-form logic within a generative model — an idea we’ll develop further when we move from unconfounded to confounded data and later when we impute potential outcomes directly. \n",
    "\n",
    "But for now let's continue to examine the relationships between these structural parameters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "
"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(2, 3, figsize=(15, 8), sharex=True, sharey=True)\n",
    "axs = axs.flatten()\n",
    "az.plot_pair(\n",
    "    idata_unconfounded[\"spike_and_slab\"],\n",
    "    var_names=[\"alpha\", \"rho\"],\n",
    "    kind=\"kde\",\n",
    "    divergences=True,\n",
    "    ax=axs[0],\n",
    ")\n",
    "az.plot_pair(\n",
    "    idata_unconfounded[\"horseshoe\"],\n",
    "    var_names=[\"alpha\", \"rho\"],\n",
    "    kind=\"kde\",\n",
    "    divergences=True,\n",
    "    ax=axs[1],\n",
    ")\n",
    "az.plot_pair(\n",
    "    idata_unconfounded[\"exclusion\"],\n",
    "    var_names=[\"alpha\", \"rho\"],\n",
    "    kind=\"kde\",\n",
    "    divergences=True,\n",
    "    ax=axs[2],\n",
    ")\n",
    "az.plot_pair(\n",
    "    idata_unconfounded[\"normal\"],\n",
    "    var_names=[\"alpha\", \"rho\"],\n",
    "    kind=\"kde\",\n",
    "    divergences=True,\n",
    "    ax=axs[3],\n",
    ")\n",
    "az.plot_pair(\n",
    "    idata_unconfounded[\"rho_tight\"],\n",
    "    var_names=[\"alpha\", \"rho\"],\n",
    "    kind=\"kde\",\n",
    "    divergences=True,\n",
    "    ax=axs[4],\n",
    ")\n",
    "az.plot_pair(\n",
    "    idata_unconfounded[\"rho_tight_spike_slab\"],\n",
    "    var_names=[\"alpha\", \"rho\"],\n",
    "    kind=\"kde\",\n",
    "    divergences=True,\n",
    "    ax=axs[5],\n",
    ")\n",
    "for ax, m in zip(\n",
    "    axs,\n",
    "    [\n",
    "        \"spike_slab\",\n",
    "        \"horse shoe\",\n",
    "        \"exclusion_restriction\",\n",
    "        \"normal\",\n",
    "        \"tight_rho\",\n",
    "        \"tight_rho_spike_slab\",\n",
    "    ],\n",
    "):\n",
    "    ax.axvline(3, linestyle=\"--\", color=\"k\", label=\"True Treatment Effect\")\n",
    "    ax.axhline(0, linestyle=\"--\", color=\"red\", label=\"True rho\")\n",
    "    ax.set_title(f\"Posterior Relationship {m}\")\n",
    "    ax.legend(loc=\"lower left\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Up to this point, we have looked at posterior summaries of individual parameters, such as the treatment effect $\\alpha$ or the correlation $\\rho$. While these marginal summaries are useful, they can obscure important interactions between parameters. In a structural model, the slope $\\alpha$ does not exist in isolation. Its interpretation depends on the joint distribution of the latent errors and the covariates that generate the treatment and outcome.\n",
    "\n",
    "The pairwise posterior plots below examine the joint distributions of $\\alpha$ and $\\rho$ and across different prior specifications. Each subplot shows the density of the posterior draws, highlighting how the inferred treatment effect co-varies with the estimated correlation between latent errors. The dashed vertical line marks the true causal effect, and the horizontal line shows the true values. \n",
    "\n",
    "By inspecting these joint distributions, we gain several insights: aggressive priors on $\\rho$ can pull the posterior of away from zero, which in turn shifts the distribution of the treatment effect estimate. But additionlly variable selection schemes like the spike-and-slab or horseshoe can significantly reduce uncertainty in the estimation of both $\\rho$ and $\\alpha$. This illustrates the trade-off between automated variable selection, prior specification. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "
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       "\n",
       "\n",
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meansdhdi_3%hdi_97%mcse_meanmcse_sdess_bulkess_tailr_hat
rho_tightalpha2.4410.2671.9912.9550.0100.010744.0838.01.01
rho0.4640.1700.1330.7660.0060.003745.0798.01.01
normalalpha3.0560.4252.2753.9100.0210.013430.0662.01.01
rho-0.0580.355-0.7220.5470.0170.008431.0690.01.01
spike_slabalpha3.0210.1402.7773.2830.0070.014781.0470.01.00
rho-0.0310.138-0.2830.2330.0060.009787.0455.01.00
horseshoealpha3.1480.2372.7833.7050.0130.012460.0303.01.01
rho-0.1470.210-0.6150.1890.0110.008461.0316.01.01
exclusion_restrictionalpha3.0190.1172.7983.2340.0030.0031445.01258.01.00
rho-0.0300.119-0.2570.1820.0030.0031464.01411.01.00
tight_rho_spike_slabalpha2.8250.1482.5533.0700.0080.010590.0364.01.00
rho0.1640.131-0.0710.4200.0060.007596.0347.01.00
\n",
       "
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      ],
      "text/plain": [
       "                              mean     sd  hdi_3%  hdi_97%  mcse_mean  \\\n",
       "rho_tight             alpha  2.441  0.267   1.991    2.955      0.010   \n",
       "                      rho    0.464  0.170   0.133    0.766      0.006   \n",
       "normal                alpha  3.056  0.425   2.275    3.910      0.021   \n",
       "                      rho   -0.058  0.355  -0.722    0.547      0.017   \n",
       "spike_slab            alpha  3.021  0.140   2.777    3.283      0.007   \n",
       "                      rho   -0.031  0.138  -0.283    0.233      0.006   \n",
       "horseshoe             alpha  3.148  0.237   2.783    3.705      0.013   \n",
       "                      rho   -0.147  0.210  -0.615    0.189      0.011   \n",
       "exclusion_restriction alpha  3.019  0.117   2.798    3.234      0.003   \n",
       "                      rho   -0.030  0.119  -0.257    0.182      0.003   \n",
       "tight_rho_spike_slab  alpha  2.825  0.148   2.553    3.070      0.008   \n",
       "                      rho    0.164  0.131  -0.071    0.420      0.006   \n",
       "\n",
       "                             mcse_sd  ess_bulk  ess_tail  r_hat  \n",
       "rho_tight             alpha    0.010     744.0     838.0   1.01  \n",
       "                      rho      0.003     745.0     798.0   1.01  \n",
       "normal                alpha    0.013     430.0     662.0   1.01  \n",
       "                      rho      0.008     431.0     690.0   1.01  \n",
       "spike_slab            alpha    0.014     781.0     470.0   1.00  \n",
       "                      rho      0.009     787.0     455.0   1.00  \n",
       "horseshoe             alpha    0.012     460.0     303.0   1.01  \n",
       "                      rho      0.008     461.0     316.0   1.01  \n",
       "exclusion_restriction alpha    0.003    1445.0    1258.0   1.00  \n",
       "                      rho      0.003    1464.0    1411.0   1.00  \n",
       "tight_rho_spike_slab  alpha    0.010     590.0     364.0   1.00  \n",
       "                      rho      0.007     596.0     347.0   1.00  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_params = pd.concat(\n",
    "    {\n",
    "        \"rho_tight\": az.summary(\n",
    "            idata_unconfounded[\"rho_tight\"], var_names=[\"alpha\", \"rho\"]\n",
    "        ),\n",
    "        \"normal\": az.summary(idata_unconfounded[\"normal\"], var_names=[\"alpha\", \"rho\"]),\n",
    "        \"spike_slab\": az.summary(\n",
    "            idata_unconfounded[\"spike_and_slab\"], var_names=[\"alpha\", \"rho\"]\n",
    "        ),\n",
    "        \"horseshoe\": az.summary(\n",
    "            idata_unconfounded[\"horseshoe\"], var_names=[\"alpha\", \"rho\"]\n",
    "        ),\n",
    "        \"exclusion_restriction\": az.summary(\n",
    "            idata_unconfounded[\"exclusion\"], var_names=[\"alpha\", \"rho\"]\n",
    "        ),\n",
    "        \"tight_rho_spike_slab\": az.summary(\n",
    "            idata_unconfounded[\"rho_tight_spike_slab\"], var_names=[\"alpha\", \"rho\"]\n",
    "        ),\n",
    "    }\n",
    ")\n",
    "\n",
    "df_params"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Similarly we can compare the models on holistic performance measures like leave-one-out cross validation. Note however, that the primary purpose here is to showcase sensitivity of the parameter of interest to model specifications. We're not necessarily seeking to enshrine one model as the best. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
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rankelpd_loop_looelpd_diffweightsedsewarningscale
horseshoe0-12664.97080817.5371080.0000005.348715e-1750.5109290.000000Falselog
rho_tight1-12665.34094720.8049850.3701394.603365e-0150.4669212.360654Falselog
normal2-12665.48558020.9611340.5147720.000000e+0050.4821972.358517Falselog
spike_and_slab3-12665.66530515.3691610.6944971.606562e-0150.5259451.331506Falselog
rho_tight_spike_slab4-12665.77371615.0983410.8029082.964150e-0150.4942251.938461Falselog
exclusion5-12666.34227818.8963041.3714708.259237e-0250.5469772.784516Falselog
\n",
       "
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      ],
      "text/plain": [
       "                      rank      elpd_loo      p_loo  elpd_diff        weight  \\\n",
       "horseshoe                0 -12664.970808  17.537108   0.000000  5.348715e-17   \n",
       "rho_tight                1 -12665.340947  20.804985   0.370139  4.603365e-01   \n",
       "normal                   2 -12665.485580  20.961134   0.514772  0.000000e+00   \n",
       "spike_and_slab           3 -12665.665305  15.369161   0.694497  1.606562e-01   \n",
       "rho_tight_spike_slab     4 -12665.773716  15.098341   0.802908  2.964150e-01   \n",
       "exclusion                5 -12666.342278  18.896304   1.371470  8.259237e-02   \n",
       "\n",
       "                             se       dse  warning scale  \n",
       "horseshoe             50.510929  0.000000    False   log  \n",
       "rho_tight             50.466921  2.360654    False   log  \n",
       "normal                50.482197  2.358517    False   log  \n",
       "spike_and_slab        50.525945  1.331506    False   log  \n",
       "rho_tight_spike_slab  50.494225  1.938461    False   log  \n",
       "exclusion             50.546977  2.784516    False   log  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "compare_df = az.compare(idata_unconfounded)\n",
    "compare_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The tables highlights the model's sensitivity to priors. Sparse priors, like spike-and-slab and horseshoe, can slightly shrink coefficients and influence the posterior spread, particularly for $\\rho$, but strong priors directly on $\\rho$ can negatively impact the estimation routine. especially when there is no true correlation. This is not a flaw. It is a feature. In practical settings, treatments and outcomes are often correlated due to unobserved confounding, measurement error, or endogenous selection. For example, in a health economics study, patients who choose a particular therapy may do so because of unobserved health determinents that also influence recovery—such as risk tolerance, underlying severity, or access to informal support. In labor economics, higher wages may appear to cause greater job satisfaction, but workers who are more motivated or more socially connected might self-select into higher-paying jobs, creating correlation between the unobserved determinants of treatment and outcome By exposing the model to different prior assumptions, we can probe how strong beliefs about sparsity or instrument validity propagate into causal estimates.\n",
    "\n",
    "In other words, prior sensitivity is a diagnostic tool as much as a regularization mechanism. When $\\rho$ is expected to be nonzero i.e. in observational studies with likely latent confounding, then explicitly modelling its distribution becomes crucial. The unconfounded case, therefore, serves as a baseline: it confirms that our joint Bayesian model can recover true parameters when the world is simple, while setting the stage for exploring more realistic, confounded scenarios where these structural dependencies must be handled carefully."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The Confounded Case\n",
    "\n",
    "While the unconfounded case provides a useful benchmark, most real-world observational studies involve some degree of endogenous treatment assignment. In our simulations, this occurs when the residuals of the treatment and outcome equations are correlated. This demonstrates that controlling only for measured variables is insufficient when unobserved confounders influence both treatment and outcome.\n",
    "\n",
    "The Bayesian joint model provides a principled solution. By explicitly modelling the correlation between treatment and outcome residuals, the framework can adjust for latent confounding while still estimating the causal slope $\\alpha$. Moreover, flexible priors such as spike-and-slab and horseshoe allow the model to automatically discover potential instruments i.e. covariates that predict the treatment but not the outcome. The theory is that the instrument structure if it holds in the world is also the one which best calibrates our parameters. These instruments help disentangle the structural effect of the treatment from latent correlations, improving identification.\n",
    "\n",
    "By setting $\\rho$ = 0.6 we simulate a moderate level of confounding—similar in spirit to cases where unmeasured preferences, abilities, or environmental factors drive both exposure and response. Conceptually, this setup mimics situations such as:\n",
    "\n",
    "-  More health-conscious individuals being both more likely to adopt a preventive therapy and more likely to recover quickly.\n",
    "\n",
    "- High-income households being more likely to invest in cleaner technologies and experience better environmental outcomes.\n",
    "\n",
    "- Firms with stronger internal capabilities both adopting new management practices and achieving higher productivity.\n",
    "\n",
    "Under such conditions, simple regression cannot disentangle correlation from causation, as the treatment is no longer independent of the unobserved outcome drivers.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "tags": [
     "hide-output"
    ]
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      "Sampling: [alpha, beta_O_raw, beta_T_raw, gamma_O_u, gamma_T_u, likelihood, m, pi_O, pi_T, rho, s, sigma_U, sigma_V]\n",
      "Initializing NUTS using jitter+adapt_diag...\n",
      "Multiprocess sampling (4 chains in 4 jobs)\n",
      "NUTS: [pi_O, beta_O_raw, gamma_O_u, pi_T, beta_T_raw, gamma_T_u, alpha, sigma_U, sigma_V, m, s, rho]\n"
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      "Sampling 4 chains for 2_000 tune and 1_000 draw iterations (8_000 + 4_000 draws total) took 487 seconds.\n",
      "There were 8 divergences after tuning. Increase `target_accept` or reparameterize.\n",
      "The rhat statistic is larger than 1.01 for some parameters. This indicates problems during sampling. See https://arxiv.org/abs/1903.08008 for details\n",
      "The effective sample size per chain is smaller than 100 for some parameters.  A higher number is needed for reliable rhat and ess computation. See https://arxiv.org/abs/1903.08008 for details\n",
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      "Sampling 4 chains for 2_000 tune and 1_000 draw iterations (8_000 + 4_000 draws total) took 255 seconds.\n",
      "There were 6 divergences after tuning. Increase `target_accept` or reparameterize.\n",
      "The rhat statistic is larger than 1.01 for some parameters. This indicates problems during sampling. See https://arxiv.org/abs/1903.08008 for details\n",
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      "Sampling 4 chains for 2_000 tune and 1_000 draw iterations (8_000 + 4_000 draws total) took 135 seconds.\n",
      "There were 7 divergences after tuning. Increase `target_accept` or reparameterize.\n",
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     "metadata": {},
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     "output_type": "stream",
     "text": [
      "Sampling 4 chains for 2_000 tune and 1_000 draw iterations (8_000 + 4_000 draws total) took 139 seconds.\n",
      "Sampling: [likelihood]\n"
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     "text": [
      "Sampling: [alpha, beta_O, beta_T, likelihood, m, rho, s, sigma_U, sigma_V]\n",
      "Initializing NUTS using jitter+adapt_diag...\n",
      "Multiprocess sampling (4 chains in 4 jobs)\n",
      "NUTS: [beta_O, beta_T, alpha, sigma_U, sigma_V, m, s, rho]\n"
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     "text": [
      "Sampling 4 chains for 2_000 tune and 1_000 draw iterations (8_000 + 4_000 draws total) took 101 seconds.\n",
      "Sampling: [likelihood]\n"
     ]
    },
    {
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     "metadata": {},
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    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Sampling: [alpha, beta_O_raw, beta_T_raw, gamma_O_u, gamma_T_u, likelihood, m, pi_O, pi_T, rho, s, sigma_U, sigma_V]\n",
      "Initializing NUTS using jitter+adapt_diag...\n",
      "Multiprocess sampling (4 chains in 4 jobs)\n",
      "NUTS: [pi_O, beta_O_raw, gamma_O_u, pi_T, beta_T_raw, gamma_T_u, alpha, sigma_U, sigma_V, m, s, rho]\n"
     ]
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       "Output()"
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      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Sampling 4 chains for 2_000 tune and 1_000 draw iterations (8_000 + 4_000 draws total) took 467 seconds.\n",
      "There were 10 divergences after tuning. Increase `target_accept` or reparameterize.\n",
      "Sampling: [likelihood]\n"
     ]
    },
    {
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      ],
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     "metadata": {},
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   ],
   "source": [
    "idata_confounded = fit_models(fit_kwargs, models_confounded)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can again compare these models on predictive performance measures, but the real focus is on the success of the causal identification within these model specifications. The performance metrics also highlight that they're broadly similar models. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
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rankelpd_loop_looelpd_diffweightsedsewarningscale
rho_tight_spike_slab0-12101.18143612.9182190.0000001.000000e+0050.3974850.000000Falselog
spike_and_slab1-12101.68234513.6713510.5009090.000000e+0050.4002170.359948Falselog
horseshoe2-12102.71375416.7721091.5323181.799291e-1650.3879381.504941Falselog
rho_tight3-12105.03554020.8156793.8541040.000000e+0050.3003122.771039Falselog
normal4-12105.10604520.8940303.9246091.863799e-1650.3326892.738899Falselog
exclusion5-12105.11505718.8937933.9336210.000000e+0050.3024042.580116Falselog
\n",
       "
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      ],
      "text/plain": [
       "                      rank      elpd_loo      p_loo  elpd_diff        weight  \\\n",
       "rho_tight_spike_slab     0 -12101.181436  12.918219   0.000000  1.000000e+00   \n",
       "spike_and_slab           1 -12101.682345  13.671351   0.500909  0.000000e+00   \n",
       "horseshoe                2 -12102.713754  16.772109   1.532318  1.799291e-16   \n",
       "rho_tight                3 -12105.035540  20.815679   3.854104  0.000000e+00   \n",
       "normal                   4 -12105.106045  20.894030   3.924609  1.863799e-16   \n",
       "exclusion                5 -12105.115057  18.893793   3.933621  0.000000e+00   \n",
       "\n",
       "                             se       dse  warning scale  \n",
       "rho_tight_spike_slab  50.397485  0.000000    False   log  \n",
       "spike_and_slab        50.400217  0.359948    False   log  \n",
       "horseshoe             50.387938  1.504941    False   log  \n",
       "rho_tight             50.300312  2.771039    False   log  \n",
       "normal                50.332689  2.738899    False   log  \n",
       "exclusion             50.302404  2.580116    False   log  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "compare_df = az.compare(idata_confounded)\n",
    "compare_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Comparing Treatment Estimates\n",
    "\n",
    "The forest plot below compares posterior estimates of the treatment effect ($\\alpha$) and the confounding correlation ($\\rho$) across model specifications when \n",
    "$\\rho = .6$ in the data-generating process. The baseline normal model (which places diffuse priors on all parameters) clearly reflects the presence of endogeneity. Its posterior mean for $\\alpha$ is biased upward relative to the true value of 3, and the estimated $\\rho$ is positive, confirming that the model detects correlation between treatment and outcome disturbances. This behaviour mirrors the familiar bias of OLS under confounding: without structural constraints or informative priors, the model attributes part of the outcome variation caused by unobserved factors to the treatment itself. This inflates and corrupts our treatment effect estimate. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "
"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = az.plot_forest(\n",
    "    [\n",
    "        idata_confounded[\"spike_and_slab\"],\n",
    "        idata_confounded[\"horseshoe\"],\n",
    "        idata_confounded[\"exclusion\"],\n",
    "        idata_confounded[\"normal\"],\n",
    "        idata_confounded[\"rho_tight\"],\n",
    "        idata_confounded[\"rho_tight_spike_slab\"],\n",
    "    ],\n",
    "    var_names=[\"alpha\", \"rho\", \"beta_O\", \"beta_T\"],\n",
    "    combined=True,\n",
    "    model_names=[\n",
    "        \"spike_slab\",\n",
    "        \"horse shoe\",\n",
    "        \"exclusion_restriction\",\n",
    "        \"normal\",\n",
    "        \"tight_rho\",\n",
    "        \"tight_rho_spike_slab\",\n",
    "    ],\n",
    "    figsize=(20, 15),\n",
    ")\n",
    "\n",
    "ax[0].axvline(3, linestyle=\"--\", color=\"k\")\n",
    "ax[0].set_title(\n",
    "    \"Comparing Parameter Estimates across Model Specifications\", fontsize=15\n",
    ");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By contrast, models that introduce structure through priors—either by tightening the prior range on $\\rho$ or imposing shrinkage on the regression coefficients—perform noticeably better. The tight-$\\rho$ models regularize the latent correlation, effectively limiting the extent to which endogeneity can distort inference, while spike-and-slab and horseshoe priors perform selective shrinkage on the covariates, allowing the model to emphasize variables that genuinely predict the treatment. This helps isolate more valid “instrument-like” components of variation, pulling the posterior of $\\alpha$ closer to the true causal effect. \n",
    "\n",
    "The exclusion-restriction specification, which enforces prior beliefs about which covariates affect only the treatment or only the outcome, performs well too. The imposed restrictions recover both the correct treatment effect and a tight estimate of residual correlation. It may be wishful thinking that this precise instrument structure is available to an analyst in the applied setting, but instrument variable designs and their imposed exclusion restrictions should be motivated by theory. Where that theory is plausible we can hope for such precise estimates.\n",
    "\n",
    "Together, these results illustrate the power of Bayesian joint modelling: even in the presence of confounding, appropriate prior structure enables partial recovery of causal effects. Importantly, the priors do not simply “fix” the bias—they make explicit the trade-offs between flexibility and identification. This transparency is one of the key advantages of Bayesian causal inference over traditional reduced-form methods.\n",
    "\n",
    "We can see similar patterns in the below pair plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "
"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(2, 3, figsize=(15, 8), sharex=True, sharey=True)\n",
    "axs = axs.flatten()\n",
    "az.plot_pair(\n",
    "    idata_confounded[\"spike_and_slab\"],\n",
    "    var_names=[\"alpha\", \"rho\"],\n",
    "    kind=\"kde\",\n",
    "    divergences=True,\n",
    "    ax=axs[0],\n",
    ")\n",
    "az.plot_pair(\n",
    "    idata_confounded[\"horseshoe\"],\n",
    "    var_names=[\"alpha\", \"rho\"],\n",
    "    kind=\"kde\",\n",
    "    divergences=True,\n",
    "    ax=axs[1],\n",
    ")\n",
    "az.plot_pair(\n",
    "    idata_confounded[\"exclusion\"],\n",
    "    var_names=[\"alpha\", \"rho\"],\n",
    "    kind=\"kde\",\n",
    "    divergences=True,\n",
    "    ax=axs[2],\n",
    ")\n",
    "az.plot_pair(\n",
    "    idata_confounded[\"normal\"],\n",
    "    var_names=[\"alpha\", \"rho\"],\n",
    "    kind=\"kde\",\n",
    "    divergences=True,\n",
    "    ax=axs[3],\n",
    ")\n",
    "az.plot_pair(\n",
    "    idata_confounded[\"rho_tight\"],\n",
    "    var_names=[\"alpha\", \"rho\"],\n",
    "    kind=\"kde\",\n",
    "    divergences=True,\n",
    "    ax=axs[4],\n",
    ")\n",
    "az.plot_pair(\n",
    "    idata_confounded[\"rho_tight_spike_slab\"],\n",
    "    var_names=[\"alpha\", \"rho\"],\n",
    "    kind=\"kde\",\n",
    "    divergences=True,\n",
    "    ax=axs[5],\n",
    ")\n",
    "for ax, m in zip(\n",
    "    axs,\n",
    "    [\n",
    "        \"spike_slab\",\n",
    "        \"horse shoe\",\n",
    "        \"exclusion_restriction\",\n",
    "        \"normal\",\n",
    "        \"tight_rho\",\n",
    "        \"tight_rho_spike_slab\",\n",
    "    ],\n",
    "):\n",
    "    ax.axvline(3, linestyle=\"--\", color=\"k\", label=\"True Treatment Effect\")\n",
    "    ax.axhline(0.6, linestyle=\"--\", color=\"red\", label=\"True rho\")\n",
    "    ax.set_title(f\"Posterior Relationship {m}\")\n",
    "    ax.legend(loc=\"lower left\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Each panel displays the joint posterior density between these two parameters for a given model specification.\n",
    "\n",
    "In the baseline normal model, the posteriors of $\\alpha$ and $\\rho$ exhibit a strong negative association: as the inferred residual correlation decreases, the estimated treatment effect increases. This pattern is characteristic of endogeneity. Part of the treatment’s apparent effect on the outcome is actually explained by unobserved factors that simultaneously drive both. The normal model correctly detects confounding but cannot disentangle its consequences without additional structure, leaving the treatment effect biased.\n",
    "\n",
    "One other feature evident from the spike and slab and horseshoe models is that the posterior distribution is somewhat bi-modal. The evidence pulls in two ways. There is not sufficient evidence in the data alone for the model to decisively characterise the $\\rho$ parameter and this induces a schizophrenic posterior distribution in the $\\alpha$ values estimated with these models. In other words, the posterior appears bi-modal. There are multiple centres of mass in the probability distribution representing a kind of indecision or oscillation between two views of the world.\n",
    "\n",
    "Introducing tight-$\\rho$ priors fundamentally changes this relationship. By constraining the allowable range of to moderate values, we effectively impose an analyst’s belief that the degree of confounding, while nonzero, is not overwhelming. This acts as a form of structural regularization: the posterior of $\\alpha$ stabilizes around the true causal effect. In practice, this mirrors what applied analysts often do implicitly. By imposing a weakly informative prior we anchor the model with plausible bounds on endogeneity rather than assuming perfect exogeneity or unbounded correlation. The preference for weakly informative priors here improves the sampling geometry but also clarifies the theoretical position of the analyst. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [
    {
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       "
meansdhdi_3%hdi_97%mcse_meanmcse_sdess_bulkess_tailr_hat
rho_tightalpha3.0920.2562.6383.5450.0090.008833.0983.01.00
rho0.5080.1760.1650.8130.0060.003839.01000.01.00
normalalpha3.5330.4072.6994.2370.0190.012486.0695.01.00
rho0.0740.394-0.6070.7620.0180.008487.0679.01.00
spike_slabalpha3.1270.4622.5433.9520.0470.018123.0592.01.05
rho0.4120.415-0.3510.8220.0430.020124.0609.01.05
horseshoealpha3.4120.4062.7163.9910.0200.006439.01413.01.01
rho0.1710.395-0.3910.7830.0200.004440.01365.01.01
exclusion_restrictionalpha2.7660.1522.4863.0580.0040.0031241.01486.01.00
rho0.7170.0620.6030.8330.0020.0011273.01519.01.00
tight_rho_spike_slabalpha2.8440.2162.4533.2480.0070.004952.01764.01.01
rho0.6710.1120.4500.8440.0040.002958.01841.01.01
\n",
       "
"
      ],
      "text/plain": [
       "                              mean     sd  hdi_3%  hdi_97%  mcse_mean  \\\n",
       "rho_tight             alpha  3.092  0.256   2.638    3.545      0.009   \n",
       "                      rho    0.508  0.176   0.165    0.813      0.006   \n",
       "normal                alpha  3.533  0.407   2.699    4.237      0.019   \n",
       "                      rho    0.074  0.394  -0.607    0.762      0.018   \n",
       "spike_slab            alpha  3.127  0.462   2.543    3.952      0.047   \n",
       "                      rho    0.412  0.415  -0.351    0.822      0.043   \n",
       "horseshoe             alpha  3.412  0.406   2.716    3.991      0.020   \n",
       "                      rho    0.171  0.395  -0.391    0.783      0.020   \n",
       "exclusion_restriction alpha  2.766  0.152   2.486    3.058      0.004   \n",
       "                      rho    0.717  0.062   0.603    0.833      0.002   \n",
       "tight_rho_spike_slab  alpha  2.844  0.216   2.453    3.248      0.007   \n",
       "                      rho    0.671  0.112   0.450    0.844      0.004   \n",
       "\n",
       "                             mcse_sd  ess_bulk  ess_tail  r_hat  \n",
       "rho_tight             alpha    0.008     833.0     983.0   1.00  \n",
       "                      rho      0.003     839.0    1000.0   1.00  \n",
       "normal                alpha    0.012     486.0     695.0   1.00  \n",
       "                      rho      0.008     487.0     679.0   1.00  \n",
       "spike_slab            alpha    0.018     123.0     592.0   1.05  \n",
       "                      rho      0.020     124.0     609.0   1.05  \n",
       "horseshoe             alpha    0.006     439.0    1413.0   1.01  \n",
       "                      rho      0.004     440.0    1365.0   1.01  \n",
       "exclusion_restriction alpha    0.003    1241.0    1486.0   1.00  \n",
       "                      rho      0.001    1273.0    1519.0   1.00  \n",
       "tight_rho_spike_slab  alpha    0.004     952.0    1764.0   1.01  \n",
       "                      rho      0.002     958.0    1841.0   1.01  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_params = pd.concat(\n",
    "    {\n",
    "        \"rho_tight\": az.summary(\n",
    "            idata_confounded[\"rho_tight\"], var_names=[\"alpha\", \"rho\"]\n",
    "        ),\n",
    "        \"normal\": az.summary(idata_confounded[\"normal\"], var_names=[\"alpha\", \"rho\"]),\n",
    "        \"spike_slab\": az.summary(\n",
    "            idata_confounded[\"spike_and_slab\"], var_names=[\"alpha\", \"rho\"]\n",
    "        ),\n",
    "        \"horseshoe\": az.summary(\n",
    "            idata_confounded[\"horseshoe\"], var_names=[\"alpha\", \"rho\"]\n",
    "        ),\n",
    "        \"exclusion_restriction\": az.summary(\n",
    "            idata_confounded[\"exclusion\"], var_names=[\"alpha\", \"rho\"]\n",
    "        ),\n",
    "        \"tight_rho_spike_slab\": az.summary(\n",
    "            idata_confounded[\"rho_tight_spike_slab\"], var_names=[\"alpha\", \"rho\"]\n",
    "        ),\n",
    "    }\n",
    ")\n",
    "\n",
    "df_params"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Across all specifications, the diagnostics tell a consistent story: effective sample sizes are high, `rhat` values hover near 1.00, and divergent transitions are minimal or absent. These are healthy traces, suggesting that the posterior geometries are well-explored and that the models are numerically stable under their respective prior assumptions.\n",
    "\n",
    "### Causal Identification and Variable Selection\n",
    "\n",
    "Before continuing to the binary case it's worth diving into the role of priors in these structural causal models. Both spike and slab and horseshoe priors were designed to perform automatic variable selection. The spike-and-slab via a latent mixture of near-zero and freely estimated components, and the horseshoe through continuous shrinkage that allows strong predictors to survive while damping weak or spurious ones. Ultimately these priors determine the multiplicative weights of the $\\beta$ coefficients in the model. By placing these variable selection priors on the weights, they are calibrated against the data so as to zero-out those variables that are not required. For a more thorough discussion of automated variable selection using priors we recommend {cite:t}`kaplan_bs_social_science` and the [pymc discourse](https://discourse.pymc.io/t/question-on-how-to-model-spike-and-slab-priors/5277) site.\n",
    "\n",
    "Plotting these posteriors vividly illustrates their behavior more clearly than describing it. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [
    {
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",
      "text/plain": [
       "
"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "az.plot_posterior(\n",
    "    idata_confounded[\"rho_tight_spike_slab\"], var_names=[\"beta_O\"], figsize=(20, 10)\n",
    ")\n",
    "plt.tight_layout();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This form of Bayesian regularization is crucial when the analyst suspects structural bias i.e. when some covariates may themselves be noise. By letting the model discover and downweight such variables, these priors act as a safeguard against overfitting endogenous structure. Bayesian variable selection is not merely a statistical convenience, but a structural choice about what relationships should be allowed to persist in the causal model. But this behavior should not be mistaken for a _magical salve_ for endogeneity. No prior, however clever, can know which variables are truly exogenous or which exclusion restrictions are defensible. That judgment must come from theory, domain expertise, and a careful causal design. \n",
    "\n",
    "Seen this way, these priors are best thought of as complements to theory, not substitutes for it. They are powerful tools for regularization and for exploring the robustness of our inferences, especially in high-dimensional or structurally ambiguous settings. Yet, they should always be deployed with a clear rationale about what the analyst believes to be the relevant sources of variation—and why."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The Binary Treatment Case\n",
    "\n",
    "In practice, theory-driven variable selection tends to be more tractable when the treatment is binary. For instance, when a treatment represents a policy adoption, a clinical intervention, or a discrete decision - like entering a program or not. In such settings, the causal question is easier to articulate in design terms: What would have happened if this unit had not received the treatment? Because the intervention is categorical, analysts can often draw on institutional knowledge or policy mechanisms to reason about which variables are genuine confounders, which might serve as instruments, and which can be safely excluded. This clarity of design focus makes the binary treatment context an ideal laboratory for contrasting structural Bayesian modeling with the potential outcomes perspective.\n",
    "\n",
    "This also allows us to explore how Bayesian joint modeling connects to the potential outcomes framework, where causal effects are conceptualized not just as slopes in a regression, but as differences in counterfactual predictions.To explore this, we adapt our earlier joint modeling setup to the binary treatment context. The model below replaces the continuous treatment equation with a latent variable formulation that links predictors to a Bernoulli decision through a logistic transformation. The latent variables $U$ and $V$ introduce correlated residuals between the outcome and treatment equations, controlled by a correlation parameter $\\rho$. This setup captures endogenous selection into the treatment."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/svg+xml": [
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "cluster2500 x 9\n",
       "\n",
       "2500 x 9\n",
       "\n",
       "\n",
       "cluster2500\n",
       "\n",
       "2500\n",
       "\n",
       "\n",
       "cluster2500 x 2\n",
       "\n",
       "2500 x 2\n",
       "\n",
       "\n",
       "clusterbeta_treatment (9)\n",
       "\n",
       "beta_treatment (9)\n",
       "\n",
       "\n",
       "clusterbeta_outcome (9)\n",
       "\n",
       "beta_outcome (9)\n",
       "\n",
       "\n",
       "\n",
       "X_data\n",
       "\n",
       "X_data\n",
       "~\n",
       "Data\n",
       "\n",
       "\n",
       "\n",
       "mu_treatment\n",
       "\n",
       "mu_treatment\n",
       "~\n",
       "Deterministic\n",
       "\n",
       "\n",
       "\n",
       "X_data->mu_treatment\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "mu_outcome\n",
       "\n",
       "mu_outcome\n",
       "~\n",
       "Deterministic\n",
       "\n",
       "\n",
       "\n",
       "X_data->mu_outcome\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "likelihood_treatment\n",
       "\n",
       "likelihood_treatment\n",
       "~\n",
       "Bernoulli\n",
       "\n",
       "\n",
       "\n",
       "t_data\n",
       "\n",
       "t_data\n",
       "~\n",
       "Data\n",
       "\n",
       "\n",
       "\n",
       "likelihood_treatment->t_data\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "likelihood_outcome\n",
       "\n",
       "likelihood_outcome\n",
       "~\n",
       "Normal\n",
       "\n",
       "\n",
       "\n",
       "y_data\n",
       "\n",
       "y_data\n",
       "~\n",
       "Data\n",
       "\n",
       "\n",
       "\n",
       "likelihood_outcome->y_data\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "t_data->mu_outcome\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "mu_treatment->likelihood_treatment\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "mu_outcome->likelihood_outcome\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "rho\n",
       "\n",
       "rho\n",
       "~\n",
       "Deterministic\n",
       "\n",
       "\n",
       "\n",
       "eps\n",
       "\n",
       "eps\n",
       "~\n",
       "Deterministic\n",
       "\n",
       "\n",
       "\n",
       "rho->eps\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "sigma_U\n",
       "\n",
       "sigma_U\n",
       "~\n",
       "Halfnormal\n",
       "\n",
       "\n",
       "\n",
       "sigma_U->likelihood_outcome\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "sigma_U->eps\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "rho_unconstr\n",
       "\n",
       "rho_unconstr\n",
       "~\n",
       "Normal\n",
       "\n",
       "\n",
       "\n",
       "rho_unconstr->rho\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "alpha\n",
       "\n",
       "alpha\n",
       "~\n",
       "Normal\n",
       "\n",
       "\n",
       "\n",
       "alpha->mu_outcome\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "eps_raw\n",
       "\n",
       "eps_raw\n",
       "~\n",
       "Normal\n",
       "\n",
       "\n",
       "\n",
       "eps_raw->eps\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "eps->mu_treatment\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "eps->mu_outcome\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "beta_T\n",
       "\n",
       "beta_T\n",
       "~\n",
       "Normal\n",
       "\n",
       "\n",
       "\n",
       "beta_T->mu_treatment\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "beta_O\n",
       "\n",
       "beta_O\n",
       "~\n",
       "Normal\n",
       "\n",
       "\n",
       "\n",
       "beta_O->mu_outcome\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       ""
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_confounded = simulate_data(n=2500, alpha_true=3, rho=0.6, cate_estimation=True)\n",
    "\n",
    "\n",
    "coords = {\n",
    "    \"beta_outcome\": [col for col in data_unconfounded.columns if \"feature\" in col],\n",
    "    \"beta_treatment\": [col for col in data_unconfounded.columns if \"feature\" in col],\n",
    "    \"obs\": range(data_unconfounded.shape[0]),\n",
    "    \"latent\": [\"U\", \"V\"],\n",
    "    \"sigmas_1\": [\"var_U\", \"cov_UV\"],\n",
    "    \"sigmas_2\": [\"cov_VU\", \"var_V\"],\n",
    "}\n",
    "\n",
    "\n",
    "def make_binary_model(\n",
    "    data,\n",
    "    coords,\n",
    "    bart_treatment=False,\n",
    "    bart_outcome=False,\n",
    "    cate_estimation=False,\n",
    "    X=None,\n",
    "    Y=None,\n",
    "    T=None,\n",
    "    priors=None,\n",
    "    observed=True,\n",
    "):\n",
    "    if X is None:\n",
    "        X = data[[col for col in data.columns if \"feature\" in col]]\n",
    "        Y = data[\"Y_bin\"].values\n",
    "        T = data[\"T_bin\"].values\n",
    "\n",
    "    if priors is None:\n",
    "        priors = {\n",
    "            \"rho\": [0, 0.5],\n",
    "            \"alpha\": [0, 10],\n",
    "            \"beta_O\": [0, 1],\n",
    "            \"eps\": [0, 1],\n",
    "            \"sigma_U\": [1],\n",
    "        }\n",
    "\n",
    "    with pm.Model(coords=coords) as binary_model:\n",
    "        X_data = pm.Data(\"X_data\", X.values)\n",
    "        y_data = pm.Data(\"y_data\", Y)\n",
    "        t_data = pm.Data(\"t_data\", T)\n",
    "\n",
    "        alpha = pm.Normal(\"alpha\", priors[\"alpha\"][0], priors[\"alpha\"][1])\n",
    "        sigma_U = pm.HalfNormal(\"sigma_U\", priors[\"sigma_U\"][0])\n",
    "        # just correlation, not full covariance\n",
    "\n",
    "        rho_unconstr = pm.Normal(\"rho_unconstr\", priors[\"rho\"][0], priors[\"rho\"][1])\n",
    "        rho = pm.Deterministic(\"rho\", pm.math.tanh(rho_unconstr))  # keep |rho|<1\n",
    "\n",
    "        inverse_rho = pm.math.sqrt(pm.math.maximum(1 - rho**2, 1e-12))\n",
    "        chol = pt.stack([[sigma_U, 0.0], [sigma_U * rho, inverse_rho]])\n",
    "\n",
    "        # --- Draw latent errors ---\n",
    "        eps_raw = pm.Normal(\n",
    "            \"eps_raw\", priors[\"eps\"][0], priors[\"eps\"][1], shape=(len(data), 2)\n",
    "        )\n",
    "        eps = pm.Deterministic(\"eps\", pt.dot(eps_raw, chol.T))\n",
    "\n",
    "        U = eps[:, 0]\n",
    "        V = eps[:, 1]\n",
    "\n",
    "        if bart_treatment:\n",
    "            mu_treatment = pmb.BART(\"mu_treatment_bart\", X=X_data, Y=t_data) + V\n",
    "        else:\n",
    "            beta_treatment = pm.Normal(\"beta_T\", 0, 1, dims=\"beta_treatment\")\n",
    "            mu_treatment = pm.Deterministic(\n",
    "                \"mu_treatment\", (X_data @ beta_treatment) + V\n",
    "            )\n",
    "        p_t = pm.math.invlogit(mu_treatment)\n",
    "        if observed:\n",
    "            _ = pm.Bernoulli(\"likelihood_treatment\", p_t, observed=t_data)\n",
    "        else:\n",
    "            _ = pm.Bernoulli(\"likelihood_treatment\", p_t)\n",
    "\n",
    "        if cate_estimation:\n",
    "            pi_O = pm.Beta(\"pi_O\", alpha=2, beta=2)\n",
    "            alpha_O_raw = pm.Normal(\"alpha_O_raw\", mu=0, sigma=2, dims=\"beta_outcome\")\n",
    "            gamma_O = relaxed_bernoulli(\n",
    "                \"gamma_O\", pi_O, temperature=0.1, dims=\"beta_outcome\"\n",
    "            )\n",
    "            alpha_interaction_outcome = pm.Deterministic(\n",
    "                \"alpha_interact\", gamma_O * alpha_O_raw, dims=\"beta_outcome\"\n",
    "            )\n",
    "            alpha = alpha + pm.math.dot(X_data, alpha_interaction_outcome)\n",
    "\n",
    "        if bart_outcome:\n",
    "            mu_outcome = pmb.BART(\"mu_outcome_bart\", X=X_data, Y=y_data) + U\n",
    "        else:\n",
    "            beta_outcome = pm.Normal(\n",
    "                \"beta_O\", priors[\"beta_O\"][0], priors[\"beta_O\"][1], dims=\"beta_outcome\"\n",
    "            )\n",
    "            mu_outcome = pm.Deterministic(\n",
    "                \"mu_outcome\", (X_data @ beta_outcome) + alpha * t_data + U\n",
    "            )\n",
    "\n",
    "        if observed:\n",
    "            _ = pm.Normal(\n",
    "                \"likelihood_outcome\", mu_outcome, sigma=sigma_U, observed=y_data\n",
    "            )\n",
    "        else:\n",
    "            _ = pm.Normal(\"likelihood_outcome\", mu_outcome, sigma=sigma_U)\n",
    "\n",
    "    return binary_model\n",
    "\n",
    "\n",
    "binary_model_bart_treatment = make_binary_model(\n",
    "    data_confounded, coords, bart_treatment=True\n",
    ")\n",
    "binary_model_bart_treatment_cate = make_binary_model(\n",
    "    data_confounded, coords, bart_treatment=True, cate_estimation=True\n",
    ")\n",
    "binary_model = make_binary_model(data_confounded, coords)\n",
    "binary_model_bart_outcome = make_binary_model(\n",
    "    data_confounded, coords, bart_outcome=True\n",
    ")\n",
    "pm.model_to_graphviz(binary_model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The nested dependency structure of the model can be seen clearly in the graph above. In the binary setting, the, $\\alpha$ parameter captures the average difference in outcomes between treated and untreated units, but as before we are aiming to capture a treatment effect estimate of 3. This model is still bivariate normal in that the latent draws of `eps_raw` are transformed to reflect the correlation encoded in $\\rho$. \n",
    "\n",
    "$$\n",
    "\\epsilon_{\\text{raw}, i} =\n",
    "\\begin{pmatrix} \\epsilon_{U,i}^{\\text{raw}} \\ \\epsilon_{V,i}^{\\text{raw}} \\end{pmatrix}\n",
    "\\sim \\mathcal{N}\\left(\\begin{pmatrix} 0 \\ 0 \\end{pmatrix}, \\mathbf{I}_2\\right)\n",
    "$$\n",
    "\n",
    "due to the dot product multiplication\n",
    "\n",
    "$$\n",
    "\n",
    "\\begin{pmatrix} U_i \\ V_i \\end{pmatrix} = \\mathbf{chol} \\cdot \\epsilon_{\\text{raw}, i} \\sim \\mathcal{N}\\left(\n",
    "\\begin{pmatrix} 0 \\ 0 \\end{pmatrix},\n",
    "\\mathbf{\\Sigma} \\right)\n",
    "\n",
    "$$\n",
    "\n",
    "This is a convenient representation for the bivariate binary case that samples quite efficiently. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "tags": [
     "hide-output"
    ]
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Sampling: [alpha, beta_O, eps_raw, likelihood_outcome, likelihood_treatment, mu_treatment_bart, rho_unconstr, sigma_U]\n",
      "Multiprocess sampling (4 chains in 4 jobs)\n",
      "CompoundStep\n",
      ">NUTS: [alpha, sigma_U, rho_unconstr, eps_raw, beta_O]\n",
      ">PGBART: [mu_treatment_bart]\n"
     ]
    },
    {
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       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "OMP: Info #276: omp_set_nested routine deprecated, please use omp_set_max_active_levels instead.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "
\n"
      ],
      "text/plain": []
     },
     "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 98 seconds.\n",
      "The rhat statistic is larger than 1.01 for some parameters. This indicates problems during sampling. See https://arxiv.org/abs/1903.08008 for details\n",
      "The effective sample size per chain is smaller than 100 for some parameters.  A higher number is needed for reliable rhat and ess computation. See https://arxiv.org/abs/1903.08008 for details\n",
      "Sampling: [alpha, beta_O, beta_T, eps_raw, likelihood_outcome, likelihood_treatment, rho_unconstr, sigma_U]\n",
      "Initializing NUTS using jitter+adapt_diag...\n",
      "Multiprocess sampling (4 chains in 4 jobs)\n",
      "NUTS: [alpha, sigma_U, rho_unconstr, eps_raw, beta_T, beta_O]\n"
     ]
    },
    {
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      "text/plain": [
       "Output()"
      ]
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     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "
\n"
      ],
      "text/plain": []
     },
     "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 27 seconds.\n",
      "Sampling: [alpha, beta_T, eps_raw, likelihood_outcome, likelihood_treatment, mu_outcome_bart, rho_unconstr, sigma_U]\n",
      "Multiprocess sampling (4 chains in 4 jobs)\n",
      "CompoundStep\n",
      ">NUTS: [alpha, sigma_U, rho_unconstr, eps_raw, beta_T]\n",
      ">PGBART: [mu_outcome_bart]\n"
     ]
    },
    {
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       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "OMP: Info #276: omp_set_nested routine deprecated, please use omp_set_max_active_levels instead.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "
\n"
      ],
      "text/plain": []
     },
     "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 96 seconds.\n",
      "The rhat statistic is larger than 1.01 for some parameters. This indicates problems during sampling. See https://arxiv.org/abs/1903.08008 for details\n",
      "The effective sample size per chain is smaller than 100 for some parameters.  A higher number is needed for reliable rhat and ess computation. See https://arxiv.org/abs/1903.08008 for details\n",
      "Sampling: [alpha, alpha_O_raw, beta_O, eps_raw, gamma_O_u, likelihood_outcome, likelihood_treatment, mu_treatment_bart, pi_O, rho_unconstr, sigma_U]\n",
      "Multiprocess sampling (4 chains in 4 jobs)\n",
      "CompoundStep\n",
      ">NUTS: [alpha, sigma_U, rho_unconstr, eps_raw, pi_O, alpha_O_raw, gamma_O_u, beta_O]\n",
      ">PGBART: [mu_treatment_bart]\n"
     ]
    },
    {
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      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "OMP: Info #276: omp_set_nested routine deprecated, please use omp_set_max_active_levels instead.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "
\n"
      ],
      "text/plain": []
     },
     "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 231 seconds.\n",
      "There were 28 divergences after tuning. Increase `target_accept` or reparameterize.\n",
      "The rhat statistic is larger than 1.01 for some parameters. This indicates problems during sampling. See https://arxiv.org/abs/1903.08008 for details\n",
      "The effective sample size per chain is smaller than 100 for some parameters.  A higher number is needed for reliable rhat and ess computation. See https://arxiv.org/abs/1903.08008 for details\n"
     ]
    }
   ],
   "source": [
    "def fit_binary_model(model):\n",
    "    with model:\n",
    "        idata = pm.sample_prior_predictive()\n",
    "        idata.extend(pm.sample(target_accept=0.95))\n",
    "    return idata\n",
    "\n",
    "\n",
    "idata_binary_model_bart_treatment = fit_binary_model(binary_model_bart_treatment)\n",
    "idata_binary_model = fit_binary_model(binary_model)\n",
    "idata_binary_bart_outcome = fit_binary_model(binary_model_bart_outcome)\n",
    "idata_binary_bart_treatment_cate = fit_binary_model(binary_model_bart_treatment_cate)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Comparing Treatment Estimates\n",
    "\n",
    "Three of our four approaches successfully recover the true causal effect of 3.0, with tight uncertainty bands and accurate confounding estimates. But when BART enters the outcome equation, the results collapse: the treatment effect estimate drops to near-zero. This is not a sampling failure. Diagnostics show healthy chains, good ESS, and converged r-hat values. The model is doing exactly what we asked it to do. The problem is what we asked the model to do!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(2, 2, figsize=(20, 6), sharex=True)\n",
    "axs = axs.flatten()\n",
    "az.plot_posterior(idata_binary_model_bart_treatment, var_names=\"alpha\", ax=axs[0])\n",
    "az.plot_posterior(idata_binary_bart_outcome, var_names=\"alpha\", ax=axs[1])\n",
    "az.plot_posterior(idata_binary_model, var_names=\"alpha\", ax=axs[2])\n",
    "az.plot_posterior(idata_binary_bart_treatment_cate, var_names=\"alpha\", ax=axs[3])\n",
    "for ax, title in zip(\n",
    "    axs, [\"bart_treatment\", \"bart_outcome\", \"no_bart\", \"bart_treatment_cate\"]\n",
    "):\n",
    "    ax.axvline(3, linestyle=\"--\", color=\"k\")\n",
    "    ax.set_title(f\"Model: {title}\")\n",
    "\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The failure stems from a fundamental tension between flexibility and causal identification. In our data generating process the treatment is strongly predicted by the covariates. The flexibility of the BART outcome model picks up on this pattern. It learns the total association and does not distinguish causal relationships from association. When we then add a structural parameter α for the treatment effect, we're asking: what is the effect of the treatment _after_ BART has already explained outcome variation using the treatment predictive features. We can see this reflected in the $\\rho$ parameter for the BART outcome model. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [
    {
     "data": {
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meansdhdi_3%hdi_97%mcse_meanmcse_sdess_bulkess_tailr_hat
linear_no_bartalpha3.5150.1233.2913.7510.0050.002595.01420.01.00
rho0.5460.0570.4390.6510.0030.001479.0964.01.00
bart_treatmentalpha3.5640.1263.3293.7940.0050.002651.01296.01.01
rho0.5190.0570.4220.6330.0020.001543.0750.01.01
bart_outcomealpha0.23010.002-17.57019.8900.1150.1807578.02527.01.00
rho0.9740.0110.9550.9920.0000.0003498.03351.01.00
bart_treatment_catealpha3.2350.1113.0303.4500.0050.002526.01247.01.01
rho0.7420.0620.6320.8620.0030.001385.0782.01.02
\n",
       "
"
      ],
      "text/plain": [
       "                            mean      sd  hdi_3%  hdi_97%  mcse_mean  mcse_sd  \\\n",
       "linear_no_bart      alpha  3.515   0.123   3.291    3.751      0.005    0.002   \n",
       "                    rho    0.546   0.057   0.439    0.651      0.003    0.001   \n",
       "bart_treatment      alpha  3.564   0.126   3.329    3.794      0.005    0.002   \n",
       "                    rho    0.519   0.057   0.422    0.633      0.002    0.001   \n",
       "bart_outcome        alpha  0.230  10.002 -17.570   19.890      0.115    0.180   \n",
       "                    rho    0.974   0.011   0.955    0.992      0.000    0.000   \n",
       "bart_treatment_cate alpha  3.235   0.111   3.030    3.450      0.005    0.002   \n",
       "                    rho    0.742   0.062   0.632    0.862      0.003    0.001   \n",
       "\n",
       "                           ess_bulk  ess_tail  r_hat  \n",
       "linear_no_bart      alpha     595.0    1420.0   1.00  \n",
       "                    rho       479.0     964.0   1.00  \n",
       "bart_treatment      alpha     651.0    1296.0   1.01  \n",
       "                    rho       543.0     750.0   1.01  \n",
       "bart_outcome        alpha    7578.0    2527.0   1.00  \n",
       "                    rho      3498.0    3351.0   1.00  \n",
       "bart_treatment_cate alpha     526.0    1247.0   1.01  \n",
       "                    rho       385.0     782.0   1.02  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.concat(\n",
    "    {\n",
    "        \"linear_no_bart\": az.summary(idata_binary_model, var_names=[\"alpha\", \"rho\"]),\n",
    "        \"bart_treatment\": az.summary(\n",
    "            idata_binary_model_bart_treatment, var_names=[\"alpha\", \"rho\"]\n",
    "        ),\n",
    "        \"bart_outcome\": az.summary(\n",
    "            idata_binary_bart_outcome, var_names=[\"alpha\", \"rho\"]\n",
    "        ),\n",
    "        \"bart_treatment_cate\": az.summary(\n",
    "            idata_binary_bart_treatment_cate, var_names=[\"alpha\", \"rho\"]\n",
    "        ),\n",
    "    }\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `bart_outcome` model places weight on the correlation between treatment and outcome rather than parcel out the share of impact into the treatment and confounding relationship. The causal effect absorbed into the covariate adjustment of the BART component, and we have a fundamental misattribution which makes recovery of structural parameter impossible in this set up. The other two BART model specifications; `bart_treatment` and `bart_treatment_cate` correctly identify the structural parameter because the BART component is used to flexibly model the treatment status. The structural parameter $\\alpha$ remains identifiable as the average or baseline effect because we've partialied out the variation in the outcome explicitly. The more traditional `linear_no_bart` model does not have the flexibility to absorb the causal effect into a non-linear component. As such, the structural parameter remains identifiable. This is one of the virtues of \"simpler\" models. \n",
    "\n",
    "### Non-Parametric Causal Inference\n",
    "\n",
    "We might worry that these parametric approaches to identifying causal effects hide the real lesson. Non-parametric approximation functions can still learn the correct expected value function and we ought to derive causal estimates via the imputation of potential outcomes."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](../_static/probabilistic_intervention_fix.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We should verify that the BART-outcome model's failure isn't merely a problem with how we've extracted the treatment effect parameter $\\alpha$. Perhaps the structural parameter collapsed, but the model could still recover causal effects through direct counterfactual imputation. Rather than interpreting a regression coefficient, we directly simulate potential outcomes:\n",
    "\n",
    "- Fit a model for $E[Y | X, T]$ (however flexible)\n",
    "- Impute $Y(1)$: Set everyone to treated, predict outcomes\n",
    "- Impute $Y(0)$: Set everyone to control, predict outcomes\n",
    "- Compute ATE: Average the difference $Y(1) - Y(0)$\n",
    "\n",
    "This approach is appealing because it doesn't require interpreting structural parameters. If the model has learned the correct conditional expectation function, counterfactual imputation should recover the true causal effect—even if $\\alpha$ itself is uninterpretable. This process of imputation is then repeated across many, many samples to derive the posterior distribution of the treatment effect. "
   ]
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   ],
   "source": [
    "def impute_potential_outcomes(model, idata, n=2500):\n",
    "    with model:\n",
    "        # Posterior predictive under treatment\n",
    "        pm.set_data({\"t_data\": np.ones(n, dtype=\"int\")})\n",
    "        Y1 = pm.sample_posterior_predictive(idata, var_names=[\"likelihood_outcome\"])\n",
    "\n",
    "        # Posterior predictive under control\n",
    "        pm.set_data({\"t_data\": np.zeros(n, dtype=\"int\")})\n",
    "        Y0 = pm.sample_posterior_predictive(idata, var_names=[\"likelihood_outcome\"])\n",
    "        ATE = (\n",
    "            Y1[\"posterior_predictive\"][\"likelihood_outcome\"]\n",
    "            - Y0[\"posterior_predictive\"][\"likelihood_outcome\"]\n",
    "        ).mean()\n",
    "        print(\"Imputed Difference in Potential Outcomes\", ATE.item())\n",
    "    return Y1, Y0, ATE.item()\n",
    "\n",
    "\n",
    "y1_bart_treatment, y0_bart_treatment, ate_bart_treatment = impute_potential_outcomes(\n",
    "    binary_model_bart_treatment, idata_binary_model_bart_treatment\n",
    ")\n",
    "\n",
    "y1_bart_outcome, y0_bart_outcome, ate_bart_outcome = impute_potential_outcomes(\n",
    "    binary_model_bart_outcome, idata_binary_bart_outcome\n",
    ")\n",
    "\n",
    "y1_no_bart, y0_no_bart, ate_linear = impute_potential_outcomes(\n",
    "    binary_model, idata_binary_model\n",
    ")\n",
    "\n",
    "y1_treatment_cate, y0_treatment_cate, ate_cate = impute_potential_outcomes(\n",
    "    binary_model_bart_treatment_cate, idata_binary_bart_treatment_cate\n",
    ")\n",
    "\n",
    "imputed_effects = pd.DataFrame(\n",
    "    {\n",
    "        \"model\": [\n",
    "            \"bart_treatment\",\n",
    "            \"bart_outcome\",\n",
    "            \"linear_no_bart\",\n",
    "            \"bart_treatment_cate\",\n",
    "        ],\n",
    "        \"ate\": [ate_bart_treatment, ate_bart_outcome, ate_linear, ate_cate],\n",
    "    }\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the above code we have applied the following process to impute potential outcomes for each individual under different treatment regimes. \n",
    "\n",
    "![](../_static/potential_outcomes.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The results are striking in their consistency. For the three successful specifications, both methods of extracting causal effects agree. For the `bart_outcome` specification the Imputation approach to causal inference also fails. This is crucial. The failure is not about how we interrogate the model, but about what the model learned during fitting."
   ]
  },
  {
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0bart_treatment3.565403
1bart_outcome-0.000389
2linear_no_bart3.514159
3bart_treatment_cate3.234281
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       "
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      ],
      "text/plain": [
       "                 model       ate\n",
       "0       bart_treatment  3.565403\n",
       "1         bart_outcome -0.000389\n",
       "2       linear_no_bart  3.514159\n",
       "3  bart_treatment_cate  3.234281"
      ]
     },
     "execution_count": 22,
     "metadata": {},
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   ],
   "source": [
    "imputed_effects"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In prediction tasks, BART's flexibility is a pure advantage. It finds patterns we didn't know to look for, captures complex interactions automatically, and often achieves superior out-of-sample accuracy. But in causal inference, this same flexibility becomes a liability when it absorbs the variation we're trying to causally attribute. The problem is **structural**: any sufficiently flexible method faces this challenge. Methods that can perfectly adapt their functional form to training data will inadvertently learn causal pathways as associational patterns, unless the structure learning is constrained to partial out the treatment influences. The stronger the relationship between the predictors of the outcome and the treatment, the more we can expect to see this collapse. Flexible outcome modelling may be useful in cases where the relationship between treatment and covariates is truly independent, but it presents a risk where the focus is on recovering treatment effects."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Conditional Average Treatment Effects\n",
    "\n",
    "The BART-treatment model demonstrated that flexibility in the treatment equation doesn't harm identification. We can also introduce flexibility in how treatment effects vary with covariates, while preserving the interpretability and identifiability of structural parameters? Our `bart_treatment_cate` model allows this by interacting the treatment parameter with the covariates. This explicitly parameterize effect heterogeneity. Unlike BART in the outcome equation (which failed because it absorbed the entire treatment signal), interaction terms allow treatment effects to vary while retaining a structural interpretation. This allows flexibility while retaining identifiability. \n",
    "\n",
    "We can see this flexibility by pulling out the ITE (individual treatment effects) estimates, using the potential outcomes imputations. We can compare the ITEs across the `bart_treatment_cate` and `linear_no_bart` models. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "
"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cate = (\n",
    "    y1_treatment_cate[\"posterior_predictive\"][\"likelihood_outcome\"]\n",
    "    - y0_treatment_cate[\"posterior_predictive\"][\"likelihood_outcome\"]\n",
    ")\n",
    "\n",
    "\n",
    "sample_cate = (\n",
    "    cate.mean(dim=(\"chain\", \"draw\"))\n",
    "    .to_dataframe()\n",
    "    .sample(100)\n",
    "    .sort_values(\"likelihood_outcome\")\n",
    ")\n",
    "\n",
    "cate_linear = (\n",
    "    y1_no_bart[\"posterior_predictive\"][\"likelihood_outcome\"]\n",
    "    - y0_no_bart[\"posterior_predictive\"][\"likelihood_outcome\"]\n",
    ")\n",
    "\n",
    "res = smf.ols(\n",
    "    \"\"\"Y_bin ~ T_bin + feature_0 + feature_1 + feature_2 + feature_3 + feature_4 + feature_5 + feature_6 + feature_7 + feature_8\"\"\",\n",
    "    data,\n",
    ").fit()\n",
    "ols_est = res.params[\"T_bin\"]\n",
    "\n",
    "fig, axs = plt.subplots(1, 2, figsize=(15, 20))\n",
    "axs = axs.flatten()\n",
    "\n",
    "ax = az.plot_forest(\n",
    "    cate_linear,\n",
    "    combined=True,\n",
    "    figsize=(15, 15),\n",
    "    coords={\"likelihood_outcome_dim_0\": sample_cate.index},\n",
    "    ax=axs[0],\n",
    ")\n",
    "axs[0].axvline(3, color=\"red\", label=\"True Treatment Effect\")\n",
    "axs[0].axvline(ols_est, color=\"darkgreen\", label=\"OLS Estimate\")\n",
    "axs[0].legend()\n",
    "axs[0].set_title(\"ITE Linear-Model estimates \\n Random Sample of 100\")\n",
    "\n",
    "ax = az.plot_forest(\n",
    "    cate,\n",
    "    combined=True,\n",
    "    figsize=(15, 10),\n",
    "    coords={\"likelihood_outcome_dim_0\": sample_cate.index},\n",
    "    ax=axs[1],\n",
    ")\n",
    "axs[1].axvline(3, color=\"red\", label=\"True Treatment Effect\")\n",
    "axs[1].axvline(ols_est, color=\"darkgreen\", label=\"OLS Estimate\")\n",
    "axs[1].set_title(\"ITE CATE-Model estimates \\n Random Sample of 100\")\n",
    "axs[1].legend()\n",
    "plt.tight_layout();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This comparison shows how the flexibility of treatment effects can be incorporated without losing interpretability. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### An Empirical Application\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will now explore an example with a real data set from the NHEFS study about the effects of quitting smoking on weight. Ultimately we will compare our estimates of treatment effects to a well specified and sensible regression model. The goal is not to provide the regression estimate as a source of truth, but to show the reasonable diversity of views we can achieve while aiming to estimate causal impact. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/svg+xml": [
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "cluster1566 x 14\n",
       "\n",
       "1566 x 14\n",
       "\n",
       "\n",
       "cluster1566\n",
       "\n",
       "1566\n",
       "\n",
       "\n",
       "cluster1566 x 2\n",
       "\n",
       "1566 x 2\n",
       "\n",
       "\n",
       "clusterbeta_treatment (14)\n",
       "\n",
       "beta_treatment (14)\n",
       "\n",
       "\n",
       "clusterbeta_outcome (14)\n",
       "\n",
       "beta_outcome (14)\n",
       "\n",
       "\n",
       "\n",
       "X_data\n",
       "\n",
       "X_data\n",
       "~\n",
       "Data\n",
       "\n",
       "\n",
       "\n",
       "mu_treatment\n",
       "\n",
       "mu_treatment\n",
       "~\n",
       "Deterministic\n",
       "\n",
       "\n",
       "\n",
       "X_data->mu_treatment\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "mu_outcome\n",
       "\n",
       "mu_outcome\n",
       "~\n",
       "Deterministic\n",
       "\n",
       "\n",
       "\n",
       "X_data->mu_outcome\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "likelihood_treatment\n",
       "\n",
       "likelihood_treatment\n",
       "~\n",
       "Bernoulli\n",
       "\n",
       "\n",
       "\n",
       "likelihood_outcome\n",
       "\n",
       "likelihood_outcome\n",
       "~\n",
       "Normal\n",
       "\n",
       "\n",
       "\n",
       "y_data\n",
       "\n",
       "y_data\n",
       "~\n",
       "Data\n",
       "\n",
       "\n",
       "\n",
       "t_data\n",
       "\n",
       "t_data\n",
       "~\n",
       "Data\n",
       "\n",
       "\n",
       "\n",
       "t_data->mu_outcome\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "mu_treatment->likelihood_treatment\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "mu_outcome->likelihood_outcome\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "rho\n",
       "\n",
       "rho\n",
       "~\n",
       "Deterministic\n",
       "\n",
       "\n",
       "\n",
       "eps\n",
       "\n",
       "eps\n",
       "~\n",
       "Deterministic\n",
       "\n",
       "\n",
       "\n",
       "rho->eps\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "sigma_U\n",
       "\n",
       "sigma_U\n",
       "~\n",
       "Halfnormal\n",
       "\n",
       "\n",
       "\n",
       "sigma_U->likelihood_outcome\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "sigma_U->eps\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "rho_unconstr\n",
       "\n",
       "rho_unconstr\n",
       "~\n",
       "Normal\n",
       "\n",
       "\n",
       "\n",
       "rho_unconstr->rho\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "alpha\n",
       "\n",
       "alpha\n",
       "~\n",
       "Normal\n",
       "\n",
       "\n",
       "\n",
       "alpha->mu_outcome\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "eps_raw\n",
       "\n",
       "eps_raw\n",
       "~\n",
       "Normal\n",
       "\n",
       "\n",
       "\n",
       "eps_raw->eps\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "eps->mu_treatment\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "eps->mu_outcome\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "beta_T\n",
       "\n",
       "beta_T\n",
       "~\n",
       "Normal\n",
       "\n",
       "\n",
       "\n",
       "beta_T->mu_treatment\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "beta_O\n",
       "\n",
       "beta_O\n",
       "~\n",
       "Normal\n",
       "\n",
       "\n",
       "\n",
       "beta_O->mu_outcome\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       ""
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import causalpy as cp\n",
    "\n",
    "df_nhefs = cp.load_data(\"nhefs\")\n",
    "\n",
    "features = [\n",
    "    \"age\",\n",
    "    \"race\",\n",
    "    \"sex\",\n",
    "    \"smokeintensity\",\n",
    "    \"smokeyrs\",\n",
    "    \"wt71\",\n",
    "    \"active_1\",\n",
    "    \"active_2\",\n",
    "    \"education_2\",\n",
    "    \"education_3\",\n",
    "    \"education_4\",\n",
    "    \"education_5\",\n",
    "    \"exercise_1\",\n",
    "    \"exercise_2\",\n",
    "]\n",
    "X = df_nhefs[features]\n",
    "X = (X - X.mean(axis=0)) / X.std(axis=0)\n",
    "Y = df_nhefs[\"outcome\"].values\n",
    "T = df_nhefs[\"trt\"].values\n",
    "\n",
    "\n",
    "coords = {\n",
    "    \"beta_outcome\": features,\n",
    "    \"beta_treatment\": features,\n",
    "    \"obs\": range(df_nhefs.shape[0]),\n",
    "    \"latent\": [\"U\", \"V\"],\n",
    "    \"sigmas_1\": [\"var_U\", \"cov_UV\"],\n",
    "    \"sigmas_2\": [\"cov_VU\", \"var_V\"],\n",
    "}\n",
    "\n",
    "priors = {\n",
    "    \"rho\": [0.0, 0.5],\n",
    "    \"alpha\": [0, 3],\n",
    "    \"beta_O\": [0, 3],\n",
    "    \"eps\": [0, 1],\n",
    "    \"sigma_U\": [0.5],\n",
    "}\n",
    "\n",
    "nhefs_binary_model = make_binary_model(\n",
    "    df_nhefs,\n",
    "    coords,\n",
    "    bart_treatment=False,\n",
    "    cate_estimation=False,\n",
    "    X=X,\n",
    "    Y=Y,\n",
    "    T=T,\n",
    "    priors=priors,\n",
    "    observed=False,\n",
    ")\n",
    "pm.model_to_graphviz(nhefs_binary_model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The model is specified without the observed outcomes deliberately. We feed in the predictor $X$ and now we validate how the model specification can recover accurate treatment effects.\n",
    "\n",
    "#### Parameter Recovery\n",
    "\n",
    "We \"forward\" sample from the system with known parameters. This generates a synthetic observation data that we will feed back into the model, to condition on data known to have been sampled from this model. This makes use of PyMC's do-syntax. We are intervening on the data generating process to set values of the parameters in the system. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Sampling: [eps_raw, likelihood_outcome, likelihood_treatment, rho_unconstr, sigma_U]\n"
     ]
    }
   ],
   "source": [
    "fixed_parameters = {\n",
    "    \"rho\": 0.6,\n",
    "    \"alpha\": 3,\n",
    "    \"beta_O\": [0, 1, 0.4, 0.3, 0.1, 0.8, 0, 0, 0, 0, 0, 0, 3, 0],\n",
    "    \"beta_T\": [1, 1.3, 0.5, 0.3, 0.7, 1.6, 0, 0.4, 0, 0, 0, 0, 0, 0],\n",
    "}\n",
    "with pm.do(nhefs_binary_model, fixed_parameters) as synthetic_model:\n",
    "    idata = pm.sample_prior_predictive(\n",
    "        random_seed=1000\n",
    "    )  # Sample from prior predictive distribution.\n",
    "    synthetic_y = idata[\"prior\"][\"likelihood_outcome\"].sel(draw=0, chain=0)\n",
    "    synthetic_t = idata[\"prior\"][\"likelihood_treatment\"].sel(draw=0, chain=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We now infer the probable parameters conditioned on the synthetic observed dats. That is, we condition our model on the data generated in our forward pass and attempt the backwards inference. Given the synthetic observations what is the most plausible parameterisation of the world-state that generated the data?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Sampling: [alpha, beta_O, beta_T, eps_raw, likelihood_outcome, likelihood_treatment, rho_unconstr, sigma_U]\n",
      "Initializing NUTS using jitter+adapt_diag...\n",
      "Multiprocess sampling (4 chains in 4 jobs)\n",
      "NUTS: [alpha, sigma_U, rho_unconstr, eps_raw, beta_T, beta_O]\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "1b8b598d400d4a48afec2bcdfcd24e58",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "
\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Sampling 4 chains for 2_000 tune and 500 draw iterations (8_000 + 2_000 draws total) took 12 seconds.\n",
      "The rhat statistic is larger than 1.01 for some parameters. This indicates problems during sampling. See https://arxiv.org/abs/1903.08008 for details\n",
      "The effective sample size per chain is smaller than 100 for some parameters.  A higher number is needed for reliable rhat and ess computation. See https://arxiv.org/abs/1903.08008 for details\n"
     ]
    }
   ],
   "source": [
    "# Infer parameters conditioned on observed data\n",
    "with pm.observe(\n",
    "    nhefs_binary_model,\n",
    "    {\"likelihood_outcome\": synthetic_y, \"likelihood_treatment\": synthetic_t},\n",
    ") as inference_model:\n",
    "    idata_sim = pm.sample_prior_predictive()\n",
    "    idata_sim.extend(pm.sample(random_seed=100, chains=4, tune=2000, draws=500))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The inferential move allows us to accurately recover the focal parameters. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "
"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = az.plot_pair(idata_sim, var_names=[\"alpha\", \"rho\"], kind=\"kde\", figsize=(15, 4))\n",
    "ax.axhline(0.6, linestyle=\"--\", color=\"red\", label=\"True Rho\")\n",
    "ax.axvline(3, linestyle=\"--\", color=\"black\", label=\"True Alpha\")\n",
    "ax.set_title(\"Parameter Recovery with NHEFS data\")\n",
    "ax.legend();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The parameter recovery is also extended to the covariate weights in the system. This is promising. It suggests that our model is able to recover true parameters from the data. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "
"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "az.plot_posterior(idata_sim, var_names=[\"beta_O\"], ref_val=fixed_parameters[\"beta_O\"]);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Conditional Update on Observed Data\n",
    "\n",
    "Next we will condition on the actual observed data and apply a range of priors to the $\\rho$ term to test the sensitivity of our findings to prior weights. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "tags": [
     "hide-output"
    ]
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Sampling: [alpha, beta_O, beta_T, eps_raw, likelihood_outcome, likelihood_treatment, rho_unconstr, sigma_U]\n",
      "Initializing NUTS using jitter+adapt_diag...\n",
      "Multiprocess sampling (4 chains in 4 jobs)\n",
      "NUTS: [alpha, sigma_U, rho_unconstr, eps_raw, beta_T, beta_O]\n",
      "OMP: Info #276: omp_set_nested routine deprecated, please use omp_set_max_active_levels instead.\n",
      "OMP: Info #276: omp_set_nested routine deprecated, please use omp_set_max_active_levels instead.\n",
      "OMP: Info #276: omp_set_nested routine deprecated, please use omp_set_max_active_levels instead.\n",
      "OMP: Info #276: omp_set_nested routine deprecated, please use omp_set_max_active_levels instead.\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e2bbec8b717f4e1788e46ef27f735768",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "
\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Sampling 4 chains for 2_000 tune and 1_000 draw iterations (8_000 + 4_000 draws total) took 36 seconds.\n",
      "Sampling: [alpha, alpha_O_raw, beta_O, eps_raw, gamma_O_u, likelihood_outcome, likelihood_treatment, mu_treatment_bart, pi_O, rho_unconstr, sigma_U]\n",
      "Multiprocess sampling (4 chains in 4 jobs)\n",
      "CompoundStep\n",
      ">NUTS: [alpha, sigma_U, rho_unconstr, eps_raw, pi_O, alpha_O_raw, gamma_O_u, beta_O]\n",
      ">PGBART: [mu_treatment_bart]\n",
      "OMP: Info #276: omp_set_nested routine deprecated, please use omp_set_max_active_levels instead.\n",
      "OMP: Info #276: omp_set_nested routine deprecated, please use omp_set_max_active_levels instead.\n",
      "OMP: Info #276: omp_set_nested routine deprecated, please use omp_set_max_active_levels instead.\n",
      "OMP: Info #276: omp_set_nested routine deprecated, please use omp_set_max_active_levels instead.\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "8786124d10b945ba8fa116f418d99cc3",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "OMP: Info #276: omp_set_nested routine deprecated, please use omp_set_max_active_levels instead.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "
\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Sampling 4 chains for 2_000 tune and 1_000 draw iterations (8_000 + 4_000 draws total) took 167 seconds.\n",
      "The rhat statistic is larger than 1.01 for some parameters. This indicates problems during sampling. See https://arxiv.org/abs/1903.08008 for details\n",
      "The effective sample size per chain is smaller than 100 for some parameters.  A higher number is needed for reliable rhat and ess computation. See https://arxiv.org/abs/1903.08008 for details\n",
      "Sampling: [alpha, beta_O, beta_T, eps_raw, likelihood_outcome, likelihood_treatment, rho_unconstr, sigma_U]\n",
      "Initializing NUTS using jitter+adapt_diag...\n",
      "Multiprocess sampling (4 chains in 4 jobs)\n",
      "NUTS: [alpha, sigma_U, rho_unconstr, eps_raw, beta_T, beta_O]\n",
      "OMP: Info #276: omp_set_nested routine deprecated, please use omp_set_max_active_levels instead.\n",
      "OMP: Info #276: omp_set_nested routine deprecated, please use omp_set_max_active_levels instead.\n",
      "OMP: Info #276: omp_set_nested routine deprecated, please use omp_set_max_active_levels instead.\n",
      "OMP: Info #276: omp_set_nested routine deprecated, please use omp_set_max_active_levels instead.\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "a562034b456e411b95954d0811ef703b",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "
\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Sampling 4 chains for 2_000 tune and 1_000 draw iterations (8_000 + 4_000 draws total) took 37 seconds.\n"
     ]
    }
   ],
   "source": [
    "sampler_kwargs = {\n",
    "    \"tune\": 2000,\n",
    "    \"draws\": 1000,\n",
    "    \"target_accept\": 0.95,\n",
    "    \"mp_ctx\": \"spawn\",\n",
    "    \"random_seed\": 1040,\n",
    "    # \"cores\": 1\n",
    "}\n",
    "priors = {\n",
    "    \"rho\": [0.0, 0.5],\n",
    "    \"alpha\": [0, 3],\n",
    "    \"beta_O\": [0, 3],\n",
    "    \"eps\": [0, 1],\n",
    "    \"sigma_U\": [0.5],\n",
    "}\n",
    "priors_no_confounding = {\n",
    "    \"rho\": [0.0, 0.001],\n",
    "    \"alpha\": [0, 3],\n",
    "    \"beta_O\": [0, 3],\n",
    "    \"eps\": [0, 1],\n",
    "    \"sigma_U\": [0.5],\n",
    "}\n",
    "\n",
    "nhefs_binary_model = make_binary_model(\n",
    "    df_nhefs,\n",
    "    coords,\n",
    "    bart_treatment=False,\n",
    "    cate_estimation=False,\n",
    "    X=X,\n",
    "    Y=Y,\n",
    "    T=T,\n",
    "    priors=priors,\n",
    "    observed=True,\n",
    ")\n",
    "nhefs_binary_model_cate = make_binary_model(\n",
    "    df_nhefs,\n",
    "    coords,\n",
    "    bart_treatment=True,\n",
    "    cate_estimation=True,\n",
    "    X=X,\n",
    "    Y=Y,\n",
    "    T=T,\n",
    "    priors=priors,\n",
    "    observed=True,\n",
    ")\n",
    "nhefs_binary_model_0_rho = make_binary_model(\n",
    "    df_nhefs,\n",
    "    coords,\n",
    "    bart_treatment=False,\n",
    "    cate_estimation=False,\n",
    "    X=X,\n",
    "    Y=Y,\n",
    "    T=T,\n",
    "    priors=priors_no_confounding,\n",
    "    observed=True,\n",
    ")\n",
    "\n",
    "with nhefs_binary_model:\n",
    "    idata_nhefs = pm.sample_prior_predictive()\n",
    "    idata_nhefs.extend(\n",
    "        pm.sample(**sampler_kwargs, idata_kwargs={\"log_likelihood\": True})\n",
    "    )\n",
    "\n",
    "with nhefs_binary_model_cate:\n",
    "    idata_nhefs_cate = pm.sample_prior_predictive()\n",
    "    idata_nhefs_cate.extend(\n",
    "        pm.sample(**sampler_kwargs, idata_kwargs={\"log_likelihood\": True})\n",
    "    )\n",
    "\n",
    "with nhefs_binary_model_0_rho:\n",
    "    idata_nhefs_0_rho = pm.sample_prior_predictive()\n",
    "    idata_nhefs_0_rho.extend(\n",
    "        pm.sample(**sampler_kwargs, idata_kwargs={\"log_likelihood\": True})\n",
    "    )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The predictive comparison shows effectively indistinguishable model performance metrics. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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rankelpd_loop_looelpd_diffweightsedsewarningscale
nhefs_binary_linear0-5320.434748696.9122060.000008.547640e-0145.8318700.000000Truelog
nhefs_bart_cate1-5330.672359685.94891110.237612.359511e-1445.9691974.569969Truelog
nhefs_rho_02-5345.348468737.17665724.913721.452360e-0145.2361128.490733Truelog
\n",
       "
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      ],
      "text/plain": [
       "                     rank     elpd_loo       p_loo  elpd_diff        weight  \\\n",
       "nhefs_binary_linear     0 -5320.434748  696.912206    0.00000  8.547640e-01   \n",
       "nhefs_bart_cate         1 -5330.672359  685.948911   10.23761  2.359511e-14   \n",
       "nhefs_rho_0             2 -5345.348468  737.176657   24.91372  1.452360e-01   \n",
       "\n",
       "                            se       dse  warning scale  \n",
       "nhefs_binary_linear  45.831870  0.000000     True   log  \n",
       "nhefs_bart_cate      45.969197  4.569969     True   log  \n",
       "nhefs_rho_0          45.236112  8.490733     True   log  "
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "compare_df = az.compare(\n",
    "    {\n",
    "        \"nhefs_binary_linear\": idata_nhefs,\n",
    "        \"nhefs_bart_cate\": idata_nhefs_cate,\n",
    "        \"nhefs_rho_0\": idata_nhefs_0_rho,\n",
    "    },\n",
    "    var_name=\"likelihood_outcome\",\n",
    ")\n",
    "\n",
    "compare_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also assess the Bayes factor i.e. the comparison of each model under a particular null hypothesis. Here we compare each model to the null of the OLS estimate. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "
"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(1, 3, figsize=(20, 6))\n",
    "axs = axs.flatten()\n",
    "\n",
    "feature_str = \" + \".join(features)\n",
    "formula = \"outcome ~ trt +\" + feature_str\n",
    "res = smf.ols(\n",
    "    formula,\n",
    "    df_nhefs,\n",
    ").fit()\n",
    "ols_est = res.params[\"trt\"]\n",
    "\n",
    "az.plot_bf(idata_nhefs, var_name=\"alpha\", ref_val=ols_est, ax=axs[0])\n",
    "az.plot_bf(idata_nhefs_cate, var_name=\"alpha\", ref_val=ols_est, ax=axs[1])\n",
    "az.plot_bf(idata_nhefs_0_rho, var_name=\"alpha\", ref_val=ols_est, ax=axs[2])\n",
    "axs[0].set_xlabel(\"\"\" alpha \\n Linear Model \"\"\")\n",
    "axs[1].set_xlabel(\"\"\" alpha \\n BART CATE Model \"\"\")\n",
    "axs[2].set_xlabel(\"\"\" alpha \\n Rho at 0 Model \"\"\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The results all indicate a positive effect on weight due to the quitting smoking. They vary slightly in the attributed effect but, interestingly even if we try to zero out the correlation between treatment and outcome the model still implies a higher effect than observed in the simpler regression model. The Bayes factor plots repor that the alternative hypothesis $\\alpha \\neq 3$ is between 5 and 12 times more likely than the null hypothesis of  $\\alpha = 3$. They also indicate the effect of Bayesian updating by the extent in which the posterior has transformed from the prior in each plot. \n",
    "\n",
    "### Applying These Methods\n",
    "\n",
    "The models demonstrated here are not recipes to be followed mechanically but frameworks for making structural assumptions explicit. Before fitting a Bayesian causal model to real data, ask yourself three questions:\n",
    "\n",
    "**First: Can I defend my causal structure theoretically?** Which variables do you believe are confounders, which are instruments, which are irrelevant? Write down your causal graph before writing down your priors. If you cannot justify exclusion restrictions through domain knowledge or institutional understanding, data-driven variable selection will not rescue you—it will merely dress speculation in statistical clothing.\n",
    "\n",
    "**Second: How sensitive are my conclusions to structural assumptions?** The confounding parameter ρ is rarely identified from observables alone. Vary your priors on ρ across plausible ranges and observe how your treatment effect estimate shifts. Fit models with normal priors, sparse priors, and theory-driven exclusions. If your causal conclusions are stable across specifications, they're robust. If they vary dramatically, that variation is real epistemic uncertainty and should be reported as such.\n",
    "\n",
    "**Third: Where have I placed flexibility, and why?** Automated variable selection and nonparametric methods are powerful tools, but flexibility in the outcome equation can absorb the causal effects you're trying to estimate. As we demonstrated with BART, sufficiently flexible outcome models learn total associations rather than structural parameters. Use flexibility in the treatment equation if needed, but keep the outcome equation constrained to interpretable causal parameters.\n",
    "\n",
    "### Conclusion\n",
    "\n",
    "These questions point to what distinguishes structural modeling from purely associational approaches. When we specify a Bayesian causal model, we write down a probabilistic program that encodes our beliefs about how data are generated—which variables influence which, how uncertainty enters, what exclusions hold. Once fitted, the model becomes a working machine we can run forward under interventions, perturb in its assumptions, and interrogate for consequences. This executable character lets us simulate alternative worlds and test the coherence of our causal story, rather than merely report coefficients.\n",
    "\n",
    "The virtue of treating causal models as probabilistic programs is twofold. First, it forces us to articulate our causal beliefs explicitly i.e. the graphical, functional, and stochastic components that make the model run. Second, it offers a disciplined way to explore what follows from those beliefs under uncertainty. Bayesian structural causal inference therefore unites an epistemic modesty with computational rigor: each model is a local, provisional machine for generating causal understanding, not a final map of the world.\n",
    "\n",
    "The credibility revolution's achievement was recognizing that causal claims require more than correlations. Causal inference requires identification strategies. These strategies try to bracket complexity through design. Bayesian structural modeling takes a complementary path: it models complexity explicitly, then explores how robust our conclusions are to structural perturbations. Both approaches succeed when we know not only how our models work, but where they stop working. \n",
    "\n",
    "Every causal model, like every fish tank, is a \"small world\" whose regularities we can nurture but never universalize. Our task is not to master the ocean, but to build clear tanks and learn when to change the water.\n",
    "\n",
    "## References\n",
    ":::{bibliography}\n",
    ":filter: docname in docnames\n",
    ":::"
   ]
  }
 ],
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