Difference-in-differences (DiD) is the most 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 the "parallel trends assumption", that on average treatment and comparison groups would have had parallel trajectories in the absence of an intervention. Historically, DiD has been considered broadly applicable and straightforward to implement, but recent years have seen rapid advancements in DiD methods. This paper reviews and synthesizes these innovations for medical and health policy researchers. We focus on four topics: (1) assessing the parallel trends assumption in health policy contexts; (2) relaxing the parallel trends assumption when appropriate; (3) employing estimators to account for staggered treatment timing; and (4) conducting robust inference for analyses in which normal-based clustered standard errors are inappropriate. For each, we explain challenges and common pitfalls in traditional DiD and modern methods available to address these issues.

翻译：差分法（DiD）是健康政策领域最常用的观察性因果推断方法，用于评估政策与项目在现实世界中的实际影响。为估计处理效应，差分法依赖于“平行趋势假设”，即在无干预的情况下，处理组与对照组平均而言会呈现平行的变化轨迹。历史上，差分法被认为具有广泛的适用性且易于实施，但近年来差分方法学领域已取得快速进展。本文为医学与健康政策研究者系统梳理并综述了这些创新性进展。我们聚焦于四个主题：（1）在健康政策情境中评估平行趋势假设；（2）在适当条件下放宽平行趋势假设；（3）采用适用于交错处理时点的估计量；（4）在基于正态分布的聚类标准误不适用时进行稳健推断。针对每个主题，我们阐释了传统差分法面临的挑战与常见缺陷，以及可用于应对这些问题的现代方法。