How much should we trust staggered difference-in-differences estimates?
解释了交错双重差分回归估计量在评估政策影响时何时以及为何存在偏差，总结了三种替代估计量，并通过重新检验已有研究显示替代估计结果往往与原文显著不同。
We explain when and how staggered difference-in-differences regression estimators, commonly applied to assess the impact of policy changes, are biased. These biases are likely to be relevant for a large portion of research settings in finance, accounting, and law that rely on staggered treatment timing, and can result in Type-I and Type-II errors. We summarize three alternative estimators developed in the econometrics and applied literature for addressing these biases, including their differences and tradeoffs. We apply these estimators to re-examine prior published results and show, in many cases, the alternative causal estimates or inferences differ substantially from prior papers.