arXiv 4 Nov 2025 · Statistics — Methodology
arXiv:2511.02632 · PDF · DOI · OpenAlex · Extracted main text
The synthetic control method estimates the causal effect by comparing the treated unit's outcomes to a weighted average of control units that closely match its pre-treatment outcomes, assuming the relationship between treated and control potential outcomes remains stable before and after treatment. However, the estimator may become unreliable when these relationships shift or when control units are highly correlated. To address these challenges, we introduce the Distributionally Robust Synthetic Control (DRoSC) method, which accommodates potential shifts in relationships and addresses high correlations among control units. The DRoSC method targets a novel causal estimand defined as the optimizer of a worst-case optimization problem considering all possible weights compatible with the pre-treatment period. When the identification conditions for the classical synthetic control method hold, the DRoSC method targets the same causal effect as the synthetic control; when these conditions are violated, we demonstrate that this new causal estimand is a conservative proxy for the non-identifiable causal effect. We further show that the DRoSC estimator's limiting distribution is non-normal and propose a novel inferential approach. We demonstrate its performance through numerical studies and an analysis of the economic impact of terrorism in the Basque Country.
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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, A. and Gardeazabal, J (2003) The economic costs of conflict: A case study of the basque country | 1.000 | 5 | 3 | 100% |
| 2 | Chernozhukov, V., Wüthrich, K., and Zhu, Y (2021) An exact and robust conformal inference method for counterfactual and synthetic controls | 0.941 | 6 | 4 | 83% |
| 3 | Li, K. T (2020) Statistical inference for average treatment effects estimated by synthetic control methods | 0.843 | 4 | 4 | 75% |
| 4 | Shi, X., Li, K., Miao, W., Hu, M., and Tchetgen Tchetgen, E (2021) Theory for identification and inference with synthetic controls: a proximal causal inference framework | 0.843 | 4 | 3 | 75% |
| 5 | Abadie, A., Diamond, A., and Hainmueller, J (2010) Synthetic control methods for comparative case studies: Estimating the effect of california’s tobacco control program | 0.737 | 3 | 3 | 67% |
| 6 | Cattaneo, M. D., Feng, Y., and Titiunik, R (2021) Prediction intervals for synthetic control methods | 0.737 | 3 | 3 | 67% |
| 7 | Ben-Michael, E., Feller, A., and Rothstein, J (2021) The augmented synthetic control method | 0.644 | 2 | 2 | 100% |
| 8 | Manski, C. F (1990) Nonparametric bounds on treatment effects | 0.644 | 2 | 2 | 100% |
| 9 | Xie, M. and Wang, P (2024) Repro samples method for a performance guaranteed inference in general and irregular inference problems | 0.511 | 4 | 2 | 25% |
| 10 | Newey, W. K. and West, K. D (1987) A simple, positive semi-definite, heteroskedasticity and autocorrelation consistent covariance matrix | 0.511 | 3 | 2 | 33% |
Showing the top 10 of 58 scored citations.