(Empirical) Bayes Approaches to Parallel Trends

2024/04/18 by Soonwoo Kwon, Jonathan Roth, Kwon, Soonwoo +1 · 1 citation
Mathematics · #Advanced Causal Inference Techniques #Artificial intelligence #Bayes' theorem #Bayesian probability #Computer science #Econometrics #Econometrics (econ.EM) #FOS: Computer and information sciences #FOS: Economics and business #Mathematics #Methodology (stat.ME) #Statistical Methods and Bayesian Inference #Statistical Methods in Clinical Trials

paper · pdf · doi:10.48550/arxiv.2404.11839

published in arXiv (Cornell University) (Cornell University)

openalex publication_date 2024/04/18 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

We consider Bayes and Empirical Bayes (EB) approaches for dealing with violations of parallel trends. In the Bayes approach, the researcher specifies a prior over both the pre-treatment violations of parallel trends δ_pre and the post-treatment violations δ_post. The researcher then updates their posterior about the post-treatment bias δ_post given an estimate of the pre-trends δ_pre. This allows them to form posterior means and credible sets for the treatment effect of interest, τ_post. In the EB approach, the prior on the violations of parallel trends is learned from the pre-treatment observations. We illustrate these approaches in two empirical applications.

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