Double/Debiased/Neyman Machine Learning of Treatment Effects
Double/Debiased/Neyman Machine Learning of Treatment Effects
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Publisher
Nashville: American Economic Association
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Language
English
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Publisher
Nashville: American Economic Association
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Contents
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 cont...
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Full title
Double/Debiased/Neyman Machine Learning of Treatment Effects
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TN_cdi_proquest_journals_1898639126
Permalink
https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_proquest_journals_1898639126
Other Identifiers
ISSN
0002-8282
E-ISSN
1944-7981
DOI
10.1257/aer.p20171038
