arXiv 30 Dec 2020 · Econometrics
arXiv:2012.15367 · PDF · DOI · OpenAlex · Extracted main text
We propose a sensitivity analysis for Synthetic Control (SC) treatment effect estimates to interrogate the assumption that the SC method is well-specified, namely that choosing weights to minimize pre-treatment prediction error yields accurate predictions of counterfactual post-treatment outcomes. Our data-driven procedure recovers the set of treatment effects consistent with the assumption that the misspecification error incurred by the SC method is at most the observable misspecification error incurred when using the SC estimator to predict the outcomes of some control unit. We show that under one definition of misspecification error, our procedure provides a simple, geometric motivation for comparing the estimated treatment effect to the distribution of placebo residuals to assess estimate credibility. When we apply our procedure to several canonical studies that report SC estimates, we broadly confirm the conclusions drawn by the source papers.
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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 | Alberto Abadie, Alexis Diamond, and Jens Hainmueller (2010) Synthetic control methods for comparative case studies: Estimating the effect of california’s tobacco control program | 1.000 | 24 | 3 | 100% |
| 2 | Alberto Abadie (2020) Using synthetic controls: Feasibility, data requirements, and methodological aspects | 1.000 | 11 | 4 | 100% |
| 3 | Matias D Cattaneo, Yingjie Feng, and Rocio Titiunik (2019) Prediction intervals for synthetic control methods | 0.941 | 6 | 4 | 83% |
| 4 | Alberto Abadie, Alexis Diamond, and Jens Hainmueller (2015) Comparative politics and the synthetic control method | 0.928 | 5 | 4 | 80% |
| 5 | Victor Chernozhukov, Kaspar Wuthrich, and Yinchu Zhu (2017) An exact and robust conformal inference method for counterfactual and synthetic controls | 0.843 | 5 | 3 | 60% |
| 6 | Victor Chernozhukov, Kaspar Wuthrich, and Yinchu Zhu (2018) Practical and robust $ t $-test based inference for synthetic control and related methods | 0.843 | 3 | 3 | 100% |
| 7 | Stephen P Boyd and Lieven Vandenberghe (2004) Convex optimization | 0.737 | 4 | 3 | 50% |
| 8 | Giovanni Peri and Vasil Yasenov (2019) The labor market effects of a refugee wave synthetic control method meets the mariel boatlift | 0.644 | 4 | 1 | 100% |
| 9 | Alberto Abadie and Jeremy L’Hour (2018) A penalized synthetic control estimator for disaggregated data | 0.644 | 3 | 2 | 67% |
| 10 | Maxwell Kellogg, Magne Mogstad, Guillaume Pouliot, and Alexander Tor… (2020) Combining matching and synthetic controls to trade off biases from extrapolation and interpolation | 0.644 | 3 | 2 | 67% |
Showing the top 10 of 27 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Distributionally Robust Synthetic Control: Ensuring Robustness Against Highly Correlated Controls and Weight Shifts | 0.405 | 1 | 1 |
| 2 | Synthetic Parallel Trends | 0.405 | 1 | 1 |