Double/Debiased/Neyman Machine Learning of Treatment Effects

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

doi:10.1257/aer.p20171038

Double/Debiased/Neyman Machine Learning of Treatment Effects

Victor Chernozhukov¹; Denis Chetverikov²; Mert Demirer¹; Esther Duflo¹; Christian Hansen³; Whitney Newey¹

¹ Massachusetts Institute of Technology, 50 Memorial Drive, Cambridge, MA 02142 (e-mail: ) · ² University of California Los Angeles, 315 Portola Plaza, Los Angeles, CA 90095 (e-mail: ) · ³ University of Chicago, 5807 S. Woodlawn Avenue, Chicago, IL 60637 (e-mail: )

American Economic Review 2017 open access

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.

DOI: 10.1257/aer.p20171038
Volume: 107 (5)
Pages: 261-265
Language: en
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Sources: crossref openalex