Abstract：
Difference-in-difference (DID) estimators are a valuable method for identifying causal effects in the public health researcher\'s toolkit. A growing methods literature points out potential problems with DID estimators when treatment is staggered in adoption and varies with time. Despite this, no practical guide exists for addressing these new critiques in public health research. We illustrate these new DID concepts with step-by-step examples, code, and a checklist. We draw insights by comparing the simple 2 × 2 DID design (single treatment group, single control group, two time periods) with more complex cases: additional treated groups, additional time periods of treatment, and treatment effects possibly varying over time. We outline newly uncovered threats to causal interpretation of DID estimates and the solutions the literature has proposed, relying on a decomposition that shows how the more complex DIDs are an average of simpler 2 × 2 DID subexperiments.
摘要：
在公共卫生研究人员的工具包中，差异（DID）估计器是一种有价值的方法，用于识别因果效应。越来越多的方法文献指出，当治疗交错采用并随时间变化时，DID估计器存在潜在问题。尽管如此,在公共卫生研究中，没有解决这些新批评的实用指南。我们用逐步的例子来说明这些新的DID概念，代码,还有一份清单.我们通过比较简单的2×2DID设计(单治疗组，单对照组,两个时间段)更复杂的病例：额外的治疗组，额外的治疗时间，和治疗效果可能随着时间的推移而变化。我们概述了新发现的对DID估计的因果解释的威胁以及文献提出的解决方案，依靠分解来显示更复杂的DID如何是更简单的2×2DID子实验的平均值。预计《公共卫生年度回顾》的最终在线发布日期，第45卷是2024年4月。请参阅http://www。annualreviews.org/page/journal/pubdates的订正估计数。
