A Practical Introduction to Bayesian Estimation of Causal Effects: Parametric and Nonparametric Approaches
arXiv:2004.07375 · doi:10.1002/sim.8761
Abstract
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.
arXiv commentCurrently under second-round revision. This version included edits from first round
References in corpus (7)
- Identification, Inference and Sensitivity Analysis for Causal Mediation Effects
- BART: Bayesian additive regression trees
- Monte Carlo Implementation of Gaussian Process Models for Bayesian Regression and Classification
- Nonlinear Models Using Dirichlet Process Mixtures
- Bayesian additive regression trees and the General BART model
- Bayesian Nonparametric Estimation for Dynamic Treatment Regimes with Sequential Transition Times
- A Bayesian Nonparametric Model for Zero-Inflated Outcomes: Prediction, Clustering, and Causal Estimation