A practical introduction to Bayesian estimation of causal effects: Parametric and nonparametric approaches

Arman Oganisian, Jason A. Roy

Research output: Contribution to journalArticlepeer-review

Abstract

Substantial advances in Bayesian methods for causal inference have been made 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 in 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 to leave the reader 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.

Original languageEnglish (US)
Pages (from-to)518-551
Number of pages34
JournalStatistics in Medicine
Volume40
Issue number2
DOIs
StatePublished - Jan 30 2021

All Science Journal Classification (ASJC) codes

  • Epidemiology
  • Statistics and Probability

Keywords

  • BART
  • Bayesian
  • Bayesian nonparametric
  • Dirichlet process
  • Gaussian process
  • causal inference
  • confounding
  • g-computation

Fingerprint Dive into the research topics of 'A practical introduction to Bayesian estimation of causal effects: Parametric and nonparametric approaches'. Together they form a unique fingerprint.

Cite this