Susan Athey, Guido Imbens, Zhaonan Qu, Davide Viviano
arXiv 29 Aug 2025 · Statistics — Methodology · publishedJournal of Applied Econometrics (2026)
arXiv:2508.21536 · PDF · DOI · OpenAlex · Extracted main text
This paper studies estimation of causal effects in a panel data setting. We introduce a new estimator, the Triply RObust Panel (TROP) estimator, that combines (i) a flexible model for the potential outcomes based on a low-rank factor structure on top of a two-way-fixed effect specification, with (ii) unit weights intended to upweight units similar to the treated units and (iii) time weights intended to upweight time periods close to the treated time periods. We study the performance of the estimator in a set of simulations designed to closely match several commonly studied real data sets. We find that there is substantial variation in the performance of the estimators across the settings considered. The proposed estimator outperforms two-way-fixed-effect/difference-in-differences, synthetic control, matrix completion and synthetic-difference-in-differences estimators. We investigate what features of the data generating process lead to this performance, and assess the relative importance of the three components of the proposed estimator. We have two recommendations. Our preferred strategy is that researchers use simulations closely matched to the data they are interested in, along the lines discussed in this paper, to investigate which estimators work well in their particular setting. A simpler approach is to use more robust estimators such as synthetic difference-in-differences or the new triply robust panel estimator which we find to substantially outperform two-way fixed effect estimators in many empirically relevant settings.
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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 | Dmitry Arkhangelsky, Susan Athey, David A Hirshberg, Guido W Imbens,… (2019) Synthetic difference in differences self | 1.000 | 14 | 4 | 100% |
| 2 | Alberto Abadie, Alexis Diamond, and Jens Hainmueller (2010) Synthetic control methods for comparative case studies: Estimating the effect of california's tobacco control program | 0.811 | 4 | 2 | 100% |
| 3 | Guido W Imbens and Davide Viviano (2023) Identification and inference for synthetic controls with confounding self | 0.811 | 4 | 2 | 100% |
| 4 | Alberto Abadie and Javier Gardeazabal (2003) The economic costs of conflict: A case study of the basque country | 0.511 | 2 | 1 | 100% |
| 5 | Jushan Bai (2009) Panel data models with interactive fixed effects | 0.511 | 2 | 1 | 100% |
| 6 | Eli Ben-Michael, Avi Feller, and Jesse Rothstein (2018) The augmented synthetic control method | 0.511 | 2 | 1 | 100% |
| 7 | Jonathan Roth, Pedro HC Sant’Anna, Alyssa Bilinski, and John Poe (2023) What’s trending in difference-in-differences? a synthesis of the recent econometrics literature | 0.511 | 2 | 1 | 100% |
| 8 | Alberto Abadie, Alexis Diamond, and Jens Hainmueller (2015) Comparative politics and the synthetic control method | 0.405 | 1 | 1 | 100% |
| 9 | Alberto Abadie, Alexis Diamond, and Jens Hainmueller (2015) Comparative politics and the synthetic control method | 0.405 | 1 | 1 | 100% |
| 10 | Sarah Abraham and Liyang Sun (2018) Estimating dynamic treatment effects in event studies with heterogeneous treatment effects | 0.405 | 1 | 1 | 100% |
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arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.