Victor Chernozhukov, Kaspar Wüthrich, Yinchu Zhu
arXiv 25 Dec 2017 · Econometrics · publishedJournal of the American Statistical Association (2021) · 159 citations (OpenAlex)
arXiv:1712.09089 · PDF · DOI · OpenAlex · Extracted main text
We introduce new inference procedures for counterfactual and synthetic control methods for policy evaluation. We recast the causal inference problem as a counterfactual prediction and a structural breaks testing problem. This allows us to exploit insights from conformal prediction and structural breaks testing to develop permutation inference procedures that accommodate modern high-dimensional estimators, are valid under weak and easy-to-verify conditions, and are provably robust against misspecification. Our methods work in conjunction with many different approaches for predicting counterfactual mean outcomes in the absence of the policy intervention. Examples include synthetic controls, difference-in-differences, factor and matrix completion models, and (fused) time series panel data models. Our approach demonstrates an excellent small-sample performance in simulations and is taken to a data application where we re-evaluate the consequences of decriminalizing indoor prostitution. Open-source software for implementing our conformal inference methods is available.
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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 | Cunningham, S. and Shah, M (2018) Decriminalizing indoor prostitution: Implications for sexual violence and public health | 0.971 | 12 | 5 | 92% |
| 2 | Doudchenko, N. and Imbens, G. W (2016) Balancing, regression, difference-in-differences and synthetic control methods: A synthesis | 0.950 | 7 | 3 | 86% |
| 3 | Abadie, A., Diamond, A., and Hainmueller, J (2010) Synthetic control methods for comparative case studies: Estimating the effect of californias tobacco control program | 0.950 | 7 | 3 | 86% |
| 4 | Abadie, A., Diamond, A., and Hainmueller, J (2015) Comparative politics and the synthetic control method | 0.843 | 5 | 4 | 60% |
| 5 | Andrews, D. W (2003) End-of-sample instability tests | 0.822 | 12 | 2 | 83% |
| 6 | Athey, S., Bayati, M., Doudchenko, N., Imbens, G., and Khosravi, K (2018) Matrix completion methods for causal panel data models | 0.811 | 4 | 2 | 100% |
| 7 | Lei, J., G'Sell, M., Rinaldo, A., Tibshirani, R. J., and Wasserman, L (2018) Distribution-free predictive inference for regression | 0.737 | 5 | 5 | 40% |
| 8 | Gobillon, L. and Magnac, T (2016) Regional policy evaluation: Interactive fixed effects and synthetic controls | 0.737 | 3 | 2 | 100% |
| 9 | Hsiao, C., Steve Ching, H., and Ki Wan, S (2012) A panel data approach for program evaluation: Measuring the benefits of political and economic integration of hong kong with mai… | 0.737 | 3 | 2 | 100% |
| 10 | Li, K. T. and Bell, D. R (2017) Estimation of average treatment effects with panel data: Asymptotic theory and implementation | 0.737 | 3 | 2 | 100% |
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