arXiv 19 Dec 2024 · Econometrics · 3 citations (OpenAlex)
arXiv:2412.14447 · PDF · DOI · OpenAlex · Extracted main text
This paper introduces the two-way common causal covariates (CCC) assumption, which is necessary to get an unbiased estimate of the ATT when using time-varying covariates in existing Difference-in-Differences methods. The two-way CCC assumption implies that the effect of the covariates remain the same between groups and across time periods. This assumption has been implied in previous literature, but has not been explicitly addressed. Through theoretical proofs and a Monte Carlo simulation study, we show that the standard TWFE and the CS-DID estimators are biased when the two-way CCC assumption is violated. We propose a new estimator called the Intersection Difference-in-differences (DID-INT) which can provide an unbiased estimate of the ATT under two-way CCC violations. DID-INT can also identify the ATT under heterogeneous treatment effects and with staggered treatment rollout. The estimator relies on parallel trends of the residuals of the outcome variable, after appropriately adjusting for covariates. This covariate residualization can recover parallel trends that are hidden with conventional estimators.
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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 | Callaway, B., and P. H. Sant’Anna (2021) Difference-in-differences with multiple time periods | 1.000 | 15 | 7 | 100% |
| 2 | Caetano, C., B. Callaway, S. Payne, and H. S. Rodrigues (2022) Difference in differences with time-varying covariates | 1.000 | 9 | 4 | 100% |
| 3 | Abadie, A (2005) Semiparametric difference-in-differences estimators | 1.000 | 8 | 3 | 100% |
| 4 | De Chaisemartin, C., and X. d’Haultfoeuille (2020) Two-way fixed effects estimators with heterogeneous treatment effects | 1.000 | 5 | 4 | 100% |
| 5 | Goodman-Bacon, A (2021) Difference-in-differences with variation in treatment timing | 0.980 | 17 | 10 | 94% |
| 6 | Bertrand, M., E. Duflo, and S. Mullainathan (2004) How much should we trust differences-in-differences estimates? | 0.928 | 4 | 3 | 100% |
| 7 | Caetano, C., and B. Callaway (2024) Difference-in-differences when parallel trends holds conditional on covariates | 0.874 | 8 | 2 | 100% |
| 8 | Heckman, J. J., H. Ichimura, and P. E. Todd (1997) Matching as an econometric evaluation estimator: Evidence from evaluating a job training programme | 0.811 | 4 | 2 | 100% |
| 9 | Borusyak, K., X. Jaravel, and J. Spiess (2024) Revisiting event-study designs: robust and efficient estimation | 0.737 | 3 | 2 | 100% |
| 10 | Deb, P., E. C. Norton, J. M. Wooldridge, and J. E. Zabel (2024) A flexible, heterogeneous treatment effects difference-in-differences estimator for repeated cross-sections, Technical report, N… | 0.737 | 3 | 2 | 100% |
Showing the top 10 of 28 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Difference-in-Differences with Unpoolable Data | 0.961 | 9 | 3 |
| 2 | Potential Outcome Modeling and Estimation in DiD Designs with Staggered Treatments | 0.405 | 1 | 1 |