# Causal Panel Analysis under Parallel Trends: Lessons from a Large Reanalysis Study

arXiv: https://arxiv.org/abs/2309.15983 · HTML: https://mlsanity.com/paper/2309.15983/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Sep 2023.

## Built on by (most cited first, 2 of 2)

- [CausalReasoningBenchmark: A Real-World Benchmark for Disentangled Evaluation of Causal Identification and Estimation](https://mlsanity.com/paper/2602.20571/) (2026)
- [A Meta-learner for Heterogeneous Effects in Difference-in-Differences](https://mlsanity.com/paper/2502.04699/) (2025)

## Abstract (arXiv)

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.