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An Exact and Robust Conformal Inference Method for Counterfactual and Synthetic Controls

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  • Victor Chernozhukov
  • Kaspar Wuthrich
  • Yinchu Zhu

Abstract

We introduce new inference procedures for counterfactual and synthetic control methods for policy evaluation. Our methods work in conjunction with many different approaches for predicting counterfactual mean outcomes in the absence of a policy intervention. Examples include synthetic controls, difference-in-differences, factor and matrix completion models, and (fused) time series panel data models. The proposed procedures are valid under weak and easy-to-verify conditions and are provably robust against misspecification. 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.

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  • Victor Chernozhukov & Kaspar Wuthrich & Yinchu Zhu, 2017. "An Exact and Robust Conformal Inference Method for Counterfactual and Synthetic Controls," Papers 1712.09089, arXiv.org, revised Nov 2019.
    • Handle: RePEc:arx:papers:1712.09089
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. Eli Ben-Michael & Avi Feller & Jesse Rothstein, 2018. "The Augmented Synthetic Control Method," Papers 1811.04170, arXiv.org, revised Nov 2019.
    2. Jianfei Cao & Connor Dowd, 2019. "Estimation and Inference for Synthetic Control Methods with Spillover Effects," Papers 1902.07343, arXiv.org, revised Nov 2019.
    3. Davide Viviano & Jelena Bradic, 2019. "Synthetic learner: model-free inference on treatments over time," Papers 1904.01490, arXiv.org.
    4. Klößner, Stefan & Pfeifer, Gregor, 2015. "Synthesizing Cash for Clunkers: Stabilizing the Car Market, Hurting the Environment," Annual Conference 2015 (Muenster): Economic Development - Theory and Policy 113207, Verein für Socialpolitik / German Economic Association.
    5. Victor Chernozhukov & Kaspar Wuthrich & Yinchu Zhu, 2018. "Practical and robust $t$-test based inference for synthetic control and related methods," Papers 1812.10820, arXiv.org, revised Jun 2019.
    6. Victor Chernozhukov & Kaspar Wüthrich & Yinchu Zhu, 2018. "Exact and robust conformal inference methods for predictive machine learning with dependent data," CeMMAP working papers CWP16/18, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    7. Ferman, Bruno, 2017. "Matching Estimators with Few Treated and Many Control Observations," MPRA Paper 78940, University Library of Munich, Germany.
    8. Bruno Ferman, 2019. "On the Properties of the Synthetic Control Estimator with Many Periods and Many Controls," Papers 1906.06665, arXiv.org, revised Oct 2019.
    9. Jason Poulos, 2019. "State-Building through Public Land Disposal? An Application of Matrix Completion for Counterfactual Prediction," Papers 1903.08028, arXiv.org.

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