 The Central Role of Bayes’ Theorem for Joint Estimation of Causal Effects and Propensity ScoresCorwin Matthew Zigler The American Statistician, 2016, vol. 70, issue 1, 47-54 Abstract: Although propensity scores have been central to the estimation of causal effects for over 30 years, only recently has the statistical literature begun to consider in detail methods for Bayesian estimation of propensity scores and causal effects. Underlying this recent body of literature on Bayesian propensity score estimation is an implicit discordance between the goal of the propensity score and the use of Bayes’ theorem. The propensity score condenses multivariate covariate information into a scalar to allow estimation of causal effects without specifying a model for how each covariate relates to the outcome. Avoiding specification of a detailed model for the outcome response surface is valuable for robust estimation of causal effects, but this strategy is at odds with the use of Bayes’ theorem, which presupposes a full probability model for the observed data that adheres to the likelihood principle. The goal of this article is to explicate this fundamental feature of Bayesian estimation of causal effects with propensity scores to provide context for the existing literature and for future work on this important topic.[Received June 2014. Revised September 2015.] Date: 2016 Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:taf:amstat:v:70:y:2016:i:1:p:47-54 Ordering information: This journal article can be ordered from DOI: 10.1080/00031305.2015.1111260 Access Statistics for this article The American Statistician is currently edited by Eric Sampson More articles in The American Statistician from Taylor & Francis Journals |
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