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...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
American Economic Association,
2018-02-21T16:48:20Z.
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| Subjects: | |
| Online Access: | Get fulltext |