ABSTRACT Difference‐in‐differences (DiD) is a popular observational causal inference method in health policy, employed to evaluate the real‐world impact of policies and programs. To estimate treatment effects, DiD relies on a “parallel trends assumption” that treatment and comparison groups would have had parallel trajectories on average in the absence of an intervention. Recent years have seen both growing use of DiD in health policy and medicine and rapid advancements in DiD methods. To support DiD implementation in these fields, this paper reviews and synthesizes best practices and recent innovations. We provide recommendations to practitioners in four areas: (1) assessing causal assumptions; (2) adjusting for covariates and other approaches to relax causal assumptions; (3) accounting for staggered treatment timing; and (4) conducting robust inference, especially when normal‐based clustered standard errors are inappropriate. For each, we explain challenges and common pitfalls in traditional DiD and recommend methods to address these. We explore current treatment of these topics through a focused literature review of medical DiD studies.
Feng et al. (Wed,) studied this question.