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

Victor Chernozhukov, Kaspar Wüthrich () and Yinchu Zhu

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Abstract: We introduce new inference procedures for counterfactual and synthetic control methods for policy evaluation. The proposed methods work in conjunction with many different approaches for predicting the counterfactual mean outcome in the absence of a policy intervention. Examples include difference-in-differences, synthetic controls, 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.

New Economics Papers: this item is included in nep-ecm
Date: 2017-12, Revised 2019-07
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Working Paper: An exact and robust conformal inference method for counterfactual and synthetic controls (2017) Downloads
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