🤖 AI Summary
The two-way fixed effects (TWFE) estimator is widely used for causal inference in political science but suffers from sensitivity to heterogeneous treatment effects (HTE) and the parallel trends (PT) assumption, undermining result reliability. This study conducts a systematic reanalysis of 49 influential political science papers employing TWFE, marking the first large-scale assessment in the discipline of six HTE-robust estimators’ stability, alongside comprehensive PT diagnostics, sensitivity analyses, and statistical power evaluation. Results show that while HTE-robust estimates are directionally consistent overall, they exhibit substantial variability; explicit PT violations are rare, yet over half the studies suffer severe statistical power deficits under joint HTE and PT constraints. The analysis reveals systematic robustness risks inherent in standard TWFE practice, providing an empirical benchmark and practical guidance for method selection and interpretation in applied political science research.
📝 Abstract
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 using TWFE models for 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.
