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Title: Bayesian causal inference: a critical review
This paper provides a critical review of the Bayesian perspective of causal inference based on the potential outcomes framework. We review the causal estimands, assignment mechanism, the general structure of Bayesian inference of causal effects and sensitivity analysis. We highlight issues that are unique to Bayesian causal inference, including the role of the propensity score, the definition of identifiability, the choice of priors in both low- and high-dimensional regimes. We point out the central role of covariate overlap and more generally the design stage in Bayesian causal inference. We extend the discussion to two complex assignment mechanisms: instrumental variable and time-varying treatments. We identify the strengths and weaknesses of the Bayesian approach to causal inference. Throughout, we illustrate the key concepts via examples. This article is part of the theme issue ‘Bayesian inference: challenges, perspectives, and prospects’.  more » « less
Award ID(s):
1945136
NSF-PAR ID:
10404779
Author(s) / Creator(s):
; ;
Date Published:
Journal Name:
Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences
Volume:
381
Issue:
2247
ISSN:
1364-503X
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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