arXiv 2 Apr 2019 · Statistics — Methodology · publishedJournal of Econometrics (2022) · 18 citations (OpenAlex)
arXiv:1904.01490 · PDF · DOI · OpenAlex · Extracted main text
Understanding the effect of a particular treatment or a policy pertains to many areas of interest, ranging from political economics, marketing to healthcare. In this paper, we develop a non-parametric algorithm for detecting the effects of treatment over time in the context of Synthetic Controls. The method builds on counterfactual predictions from many algorithms without necessarily assuming that the algorithms correctly capture the model. We introduce an inferential procedure for detecting treatment effects and show that the testing procedure is asymptotically valid for stationary, beta mixing processes without imposing any restriction on the set of base algorithms under consideration. We discuss consistency guarantees for average treatment effect estimates and derive regret bounds for the proposed methodology. The class of algorithms may include Random Forest, Lasso, or any other machine-learning estimator. Numerical studies and an application illustrate the advantages of the method.
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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 | Chernozhukov, V., K. Wüthrich, and Y. Zhu (2021) An exact and robust conformal inference method for counterfactual and synthetic controls | 1.000 | 6 | 3 | 100% |
| 2 | Chernozhukov, V., K. Wuthrich, and Y. Zhu (2018) A $ t $-test for synthetic controls | 1.000 | 5 | 3 | 100% |
| 3 | Abadie, A., A. Diamond, and J. Hainmueller (2010) Synthetic control methods for comparative case studies: Estimating the effect of california’s tobacco control program | 0.941 | 6 | 4 | 83% |
| 4 | Arkhangelsky, D., S. Athey, D. A. Hirshberg, G. W. Imbens, and S. Wa… (2021) Synthetic difference-in-differences | 0.928 | 4 | 3 | 100% |
| 5 | Chernozhukov, V., K. Wüthrich, and Z. Yinchu (2018) Exact and robust conformal inference methods for predictive machine learning with dependent data | 0.928 | 4 | 3 | 100% |
| 6 | Carvalho, C., R. Masini, and M. C. Medeiros (2018) Arco: an artificial counterfactual approach for high-dimensional panel time-series data | 0.874 | 5 | 2 | 100% |
| 7 | Cesa-Bianchi, N., G. Lugosi, et al (1999) On prediction of individual sequences | 0.843 | 4 | 3 | 75% |
| 8 | Doudchenko, N. and G. W. Imbens (2016) Balancing, regression, difference-in-differences and synthetic control methods: A synthesis | 0.843 | 3 | 3 | 100% |
| 9 | Ferman, B. and C. Pinto (2016) Revisiting the synthetic control estimator | 0.843 | 3 | 3 | 100% |
| 10 | Imai, K. and I. S. Kim (2021) On the use of two-way fixed effects regression models for causal inference with panel data | 0.843 | 3 | 3 | 100% |
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