Dennis Shen, Peng Ding, Jasjeet Sekhon, Bin Yu
arXiv 29 Jul 2022 · Econometrics · publishedEconometrica (2023) · 10 citations (OpenAlex)
arXiv:2207.14481 · PDF · DOI · OpenAlex · Extracted main text
A central goal in social science is to evaluate the causal effect of a policy. One dominant approach is through panel data analysis in which the behaviors of multiple units are observed over time. The information across time and space motivates two general approaches: (i) horizontal regression (i.e., unconfoundedness), which exploits time series patterns, and (ii) vertical regression (e.g., synthetic controls), which exploits cross-sectional patterns. Conventional wisdom states that the two approaches are fundamentally different. We establish this position to be partly false for estimation but generally true for inference. In particular, we prove that both approaches yield identical point estimates under several standard settings. For the same point estimate, however, each approach quantifies uncertainty with respect to a distinct estimand. In turn, the confidence interval developed for one estimand may have incorrect coverage for another. This emphasizes that the source of randomness that researchers assume has direct implications for the accuracy of inference.
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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 | Abadie, Alberto, Alexis Diamond, and Jens Hainmueller (2015) Comparative Politics and the Synthetic Control Method | 1.000 | 5 | 4 | 100% |
| 2 | Athey, Susan, Mohsen Bayati, Nikolay Doudchenko, Guido Imbens, and K… (2021) Matrix Completion Methods for Causal Panel Data Models | 0.874 | 7 | 2 | 100% |
| 3 | Agarwal, Anish, Devavrat Shah, and Dennis Shen (2021) Synthetic Interventions self | 0.874 | 5 | 2 | 100% |
| 4 | Abadie, Alberto, Alexis Diamond, and Jens Hainmueller (2010) Synthetic Control Methods for Comparative Case Studies: Estimating the Effect of Californiaâs Tobacco Control Program | 0.843 | 3 | 3 | 100% |
| 5 | Abadie, A. and J. Gardeazabal (2003) The Economic Costs of Conflict: A Case Study of the Basque Country | 0.811 | 4 | 2 | 100% |
| 6 | Li, Kathleen T (2020) Statistical Inference for Average Treatment Effects Estimated by Synthetic Control Methods | 0.737 | 3 | 2 | 100% |
| 7 | Li, Kathleen T. and David R. Bell (2017) Estimation of average treatment effects with panel data: Asymptotic theory and implementation | 0.737 | 3 | 2 | 100% |
| 8 | Chernozhukov, Victor, Kaspar Wüthrich, and Yinchu Zhu (2021) An Exact and Robust Conformal Inference Method for Counterfactual and Synthetic Controls | 0.737 | 3 | 2 | 100% |
| 9 | Bottmer, Lea, Guido Imbens, Jann Spiess, and Merrill Warnick (2021) A Design-Based Perspective on Synthetic Control Methods | 0.693 | 6 | 1 | 100% |
| 10 | Ben-Michael, Eli, Avi Feller, and Jesse Rothstein (2021) The Augmented Synthetic Control Method | 0.644 | 4 | 1 | 100% |
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