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Published August 7, 2017 | Submitted
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Identifying Treatment Effects under Data Combination

Abstract

We consider the identification of counterfactual distributions and treatment effects when the outcome variables and conditioning covariates are observed in separate datasets. Under the standard selection on observables assumption, the counterfactual distributions and treatment effect parameters are no longer point identified. However, applying the classical monotone re-arrangement inequality, we derive sharp bounds on the counterfactual distributions and policy parameters of interest.

Additional Information

We are grateful to Cheng Hsiao, Sergio Firpo, Marc Henry, Chuck Manski, Kevin Song, and Jeff Wooldridge for valuable comments and discussions. We thank SangMok Lee for excellent research assistance, and seminar participants at Michigan State, USC, U. Washington, the Canadian Econometrics Study Group meetings (2011, Toronto), and the Vanderbilt conference, Identification and Inference in Microecononetrics (2012) for useful comments.

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Created:
August 19, 2023
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