Matias D. Cattaneo, Yingjie Feng, Rocio Titiunik
arXiv 15 Dec 2019 · Statistics — Methodology · publishedJournal of the American Statistical Association (2021) · 18 citations (OpenAlex)
arXiv:1912.07120 · PDF · DOI · OpenAlex · Extracted main text
Uncertainty quantification is a fundamental problem in the analysis and interpretation of synthetic control (SC) methods. We develop conditional prediction intervals in the SC framework, and provide conditions under which these intervals offer finite-sample probability guarantees. Our method allows for covariate adjustment and non-stationary data. The construction begins by noting that the statistical uncertainty of the SC prediction is governed by two distinct sources of randomness: one coming from the construction of the (likely misspecified) SC weights in the pre-treatment period, and the other coming from the unobservable stochastic error in the post-treatment period when the treatment effect is analyzed. Accordingly, our proposed prediction intervals are constructed taking into account both sources of randomness. For implementation, we propose a simulation-based approach along with finite-sample-based probability bound arguments, naturally leading to principled sensitivity analysis methods. We illustrate the numerical performance of our methods using empirical applications and a small simulation study. Python, R and Stata software packages implementing our methodology are available.
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| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Chernozhukov, V., Wüthrich, K., and Zhu, Y (2021) b), An Exact and Robust Conformal Inference Method for Counterfactual and Synthetic Controls | 1.000 | 6 | 5 | 100% |
| 2 | Abadie, A., Diamond, A., and Hainmueller, J (2010) Synthetic control methods for comparative case studies: Estimating the effect of California’s tobacco control program | 0.928 | 4 | 3 | 100% |
| 3 | Abadie, A., and Gardeazabal, J (2003) The Economic Costs of Conflict: A Case Study of the Basque Country | 0.811 | 4 | 2 | 100% |
| 4 | Abadie, A (2021) Using Synthetic Controls: Feasibility, Data Requirements, and Methodological Aspects | 0.644 | 2 | 2 | 100% |
| 5 | Cattaneo, M., Feng, Y., Palomba, F., and Titiunik, R (2021) scpi: Uncertainty Quantification for Synthetic Control Estimators self | 0.644 | 2 | 2 | 100% |
| 6 | Chernozhukov, V., Wüthrich, K., and Zhu, Y (2021) a), Distributional Conformal Prediction | 0.644 | 2 | 2 | 100% |
| 7 | Doukhan, P (2012) Mixing: Properties and Examples | 0.644 | 2 | 2 | 100% |
| 8 | Raic, M (2019) A Multivariate Berry–Esseen Theorem with Explicit Constants | 0.644 | 2 | 2 | 100% |
| 9 | Vershynin, R (2018) High-Dimensional Probability: An Introduction with Applications in Data Science | 0.644 | 2 | 2 | 100% |
| 10 | Vovk, V (2012) Conditional Validity of Inductive Conformal Predictors, in | 0.644 | 2 | 2 | 100% |
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