Albert Chiu, Xingchen Lan, Ziyi Liu, Yiqing Xu
arXiv 27 Sep 2023 · Statistics — Methodology · publishedAmerican Political Science Review (2025) · 22 citations (OpenAlex)
arXiv:2309.15983 · PDF · DOI · OpenAlex · Extracted main text
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.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Borusyak, Jaravel \ Spiess (2024) Revisiting event-study designs: robust and efficient estimation | 1.000 | 8 | 3 | 100% |
| 2 | Liu, Wang \ Xu (2024) A practical guide to counterfactual estimators for causal inference with time-series cross-sectional data | 1.000 | 7 | 4 | 100% |
| 3 | de Chaisemartin \ D'Haultfuille (2020) Two-Way Fixed Effects Estimators with Heterogeneous Treatment Effects | 1.000 | 6 | 3 | 100% |
| 4 | Imai, Kim \ Wang (2023) Matching Methods for Causal Inference with Time-Series Cross-Sectional Data | 1.000 | 6 | 3 | 100% |
| 5 | Callaway \ Sant’Anna (2021) Difference-in-Differences with Multiple Time Periods | 1.000 | 6 | 3 | 100% |
| 6 | Sun \ Abraham (2021) Estimating Dynamic Treatment Effects in Event Studies with Heterogeneous Treatment Effects | 1.000 | 5 | 3 | 100% |
| 7 | de Chaisemartin \ D'Haultfuille (2024) Difference-in-Differences Estimators of Intertemporal Treatment Effects | 0.928 | 4 | 3 | 100% |
| 8 | Rambachan \ Roth (2023) A More Credible Approach to Parallel Trends | 0.928 | 4 | 3 | 100% |
| 9 | Grumbach \ Sahn (2020) Race and Representation in Campaign Finance | 0.874 | 7 | 2 | 100% |
| 10 | Baker, Larcker \ Wang (2022) How Much Should We Trust Staggered Difference-in-Differences Estimates? | 0.811 | 4 | 2 | 100% |
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