A Practical Introduction to Bayesian Estimation of Causal Effects: Parametric and Nonparametric Approaches
This is an incremental tutorial for practicing statisticians to apply Bayesian causal inference in practical settings.
The paper provides an introduction to Bayesian methods for causal inference, demonstrating how priors can induce shrinkage and sparsity in parametric models and be used for sensitivity analyses, with implementation examples using open-source software.
Substantial advances in Bayesian methods for causal inference have been developed in recent years. We provide an introduction to Bayesian inference for causal effects for practicing statisticians who have some familiarity with Bayesian models and would like an overview of what it can add to causal estimation in practical settings. In the paper, we demonstrate how priors can induce shrinkage and sparsity on parametric models and be used to perform probabilistic sensitivity analyses around causal assumptions. We provide an overview of nonparametric Bayesian estimation and survey their applications in the causal inference literature. Inference in the point-treatment and time-varying treatment settings are considered. For the latter, we explore both static and dynamic treatment regimes. Throughout, we illustrate implementation using off-the-shelf open source software. We hope the reader will walk away with implementation-level knowledge of Bayesian causal inference using both parametric and nonparametric models. All synthetic examples and code used in the paper are publicly available on a companion GitHub repository.