Double/Debiased/Neyman Machine Learning of Treatment Effects
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aer.p20171038.pdf
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Author(s)
Chetverikov, Denis
Hansen, Christian
Chernozhukov, Victor V
Demirer, Mert
Duflo, Esther
Newey, Whitney K
Date Issued
May 2017
Journal
American Economic Review
Publisher
American Economic Association
Citation
Chernozhukov, Victor et al. “Double/Debiased/Neyman Machine Learning of Treatment Effects.” American Economic Review 107, 5 (May 2017): 261–265 © 2017 American Economic Association
Version
Final published version
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.
MIT Department
Massachusetts Institute of Technology. Department of Economics
Sloan School of Management
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Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
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DOI of Published Version
https://doi.org/10.1257/AER.P20171038
