Double/Debiased/Neyman Machine Learning of Treatment Effects
Victor Chernozhukov, Denis Chetverikov, Mert Demirer, Esther Duflo, Christian Hansen, Whitney Newey
American Economic Review · 2017 · Volume 107 · Issue 5
Abstract
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.
Keywords
Single Equation Models, Single Variables: Cross-Sectional Models, Spatial Models, Treatment Effect Models, Quantile Regressions, Multiple or Simultaneous Equation Models: Cross-Sectional Models, Social Interaction Models
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