Two-way fixed effects (TWFE) models are widely used in political science to establish causality, but recent methodological discussions highlight their limitations under heterogeneous treatment effects (HTE) and violations of the parallel trends (PT) assumption. This growing literature has introduced numerous new estimators and procedures, causing confusion among researchers about the reliability of existing results and best practices. To address these concerns, we replicated and reanalyzed 49 studies from leading journals that employ TWFE models for causal inference using observational panel data with binary treatments. Using six HTE-robust estimators, diagnostic tests, and sensitivity analyses, we find: (i) HTE-robust estimators yield qualitatively similar but highly variable results; (ii) while a few studies show clear signs of PT violations, many lack evidence to support this assumption; and (iii) many studies are underpowered when accounting for HTE and potential PT violations. We emphasize the importance of strong research designs and rigorous validation of key identifying assumptions.

翻译：双向固定效应（TWFE）模型在政治学中被广泛用于建立因果关系，但近期的研究方法讨论揭示了其在异质性处理效应（HTE）和平行趋势（PT）假设违反情况下的局限性。这一不断增长的文献引入了大量新的估计量和程序，导致研究者对现有结果的可靠性和最佳实践感到困惑。为应对这些问题，我们复制并重新分析了来自顶尖期刊的49项研究，这些研究均采用TWFE模型，利用具有二元处理的观测面板数据进行因果推断。通过使用六种HTE稳健估计量、诊断检验和敏感性分析，我们发现：（i）HTE稳健估计量产生定性相似但高度可变的结果；（ii）虽然少数研究显示出明显的PT违反迹象，但许多研究缺乏支持该假设的证据；（iii）在考虑HTE和潜在的PT违反时，许多研究存在统计效力不足的问题。我们强调强健的研究设计以及对关键识别假设进行严格验证的重要性。