Double/Debiased/Neyman Machine Learning of Treatment Effects

2017/05/01 by Victor Chernozhukov, Denis Chetverikov, Mert Demirer +4 · 63 citations
Mathematics · #Advanced Causal Inference Techniques #Statistical Methods and Inference #Statistical Methods and Bayesian Inference

paper · pdf · doi:10.1257/aer.p20171038

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.

Citations

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