Double/Debiased/Neyman Machine Learning of Treatment Effects

• Main Authors: Chernozhukov, Victor, Chetverikov, Denis, Demirer, Mert, Duflo, Esther, Hansen, Christian, Newey, Whitney
• Format: Report
• Language: unknown
• Published: arXiv 2017
• Subjects: Machine Learning stat.ML; Methodology stat.ME; FOS Computer and information sciences; DML
• Online Access: https://dx.doi.org/10.48550/arxiv.1701.08687; https://arxiv.org/abs/1701.08687

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