Double/Debiased/Neyman Machine Learning of Treatment Effects

Double/Debiased/Neyman Machine Learning of Treatment Effects

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 ill...

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Bibliographic Details
Main Authors: Chetverikov, Denis, Hansen, Christian, Chernozhukov, Victor V, Demirer, Mert, Duflo, Esther, Newey, Whitney K
Other Authors: Massachusetts Institute of Technology. Department of Economics
Format: Article
Published: American Economic Association 2018
Online Access:http://hdl.handle.net/1721.1/113852
https://orcid.org/0000-0002-3250-6714
https://orcid.org/0000-0003-4300-4258
https://orcid.org/0000-0001-6105-617X
https://orcid.org/0000-0003-2699-4704

Internet

http://hdl.handle.net/1721.1/113852
https://orcid.org/0000-0002-3250-6714
https://orcid.org/0000-0003-4300-4258
https://orcid.org/0000-0001-6105-617X
https://orcid.org/0000-0003-2699-4704

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