Double/Debiased/Neyman Machine Learning of Treatment Effects
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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.
Date issued
2017-05Department
Massachusetts Institute of Technology. Department of Economics; Sloan School of ManagementJournal
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
ISSN
0002-8282
1944-7981