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Double/Debiased/Neyman Machine Learning of Treatment Effects

Victor Chernozhukov, Denis Chetverikov, Mert Demirer, Esther Duflo, Christian Hansen and Whitney Newey

American Economic Review, 2017, vol. 107, issue 5, 261-65

Abstract: Chernozhukov et al. (2016) provide a generic double/de-biased machine learning (ML) approach for obtaining valid inferential statements about focal parameters, using Neyman-orthogonal scores and cross-fitting, in settings where nuisance parameters are estimated using ML methods. In this note, we illustrate the application of this method in the context of estimating average treatment effects and average treatment effects on the treated using observational data.

JEL-codes: C21 C31 (search for similar items in EconPapers)
Date: 2017
Note: DOI: 10.1257/aer.p20171038
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