Double/Debiased/Neyman Machine Learning of Treatment Effects
Christian Hansen and
American Economic Review, 2017, vol. 107, issue 5, 261-65
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.
JEL-codes: C21 C31 (search for similar items in EconPapers)
Note: DOI: 10.1257/aer.p20171038
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