Double/Debiased/Neyman Machine Learning of Treatment Effects
展示了Chernozhukov等人(2016)提出的双重/去偏机器学习方法在观察性数据中估计平均处理效应和处理组平均处理效应的应用，该方法利用内曼正交分数和交叉拟合来获得有效的推断。
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.