Double/Debiased/Neyman Machine Learning of Treatment Effects

2017/01/30 by Victor Chernozhukov, Chernozhukov, Victor, Denis Chetverikov +10 · 17 citations
Mathematics · #Advanced Causal Inference Techniques #FOS: Computer and information sciences #Machine Learning (stat.ML) #Methodology (stat.ME) #Statistical Methods and Bayesian Inference #Statistical Methods and Inference #stat.ME #stat.ML

paper · pdf · doi:10.48550/arxiv.1701.08687

Conference paper, forthcoming in American Economic Review, Papers and Proceedings, 2017. arXiv admin note: text overlap with arXiv:1608.00060

arxiv created 2017/01/30 · openalex publication_date 2017/01/30 · arxiv updated 2017/02/06 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

Chernozhukov, Chetverikov, Demirer, Duflo, Hansen, and Newey (2016) provide a generic double/de-biased machine learning (DML) approach for obtaining valid inferential statements about focal parameters, using Neyman-orthogonal scores and cross-fitting, in settings where nuisance parameters are estimated using a new generation of nonparametric fitting methods for high-dimensional data, called machine learning methods. In this note, we illustrate the application of this method in the context of estimating average treatment effects (ATE) and average treatment effects on the treated (ATTE) using observational data. A more general discussion and references to the existing literature are available in Chernozhukov, Chetverikov, Demirer, Duflo, Hansen, and Newey (2016).

Citations

• The central role of the propensity score in observational studies for causal effects
• Double/Debiased Machine Learning for Treatment and Causal Parameters

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