Abstract. Chernozhukov, Chetverikov, Demirer, Duflo, Hansen, and Newey (2016) provide a generic double/debiased 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). Key words: Neyman machine learning, orthogonalization, cross-fitting, double or de-biased machine learning, orthogonal score, efficient score, post-machine-learning and post-regularization inference, random forest, lasso, deep learning, neural nets, boosted trees, efficiency, optimality. We consider estimation of ATE and ATTE under the unconfoundedness assumption of Rosenbaum and Rubin (1983) . We consider the case where treatment effects are fully heterogeneous and the treatment variable, D, is binary, D ∈ {0, 1}. We let Y denote the outcome variable of interest and Z denote a set of control variables. We then model random vector (Y, D, Z) as Since D is not additively separable, this model allows for very general heterogeneity in treatment effects. Common target parameters θ 0 in this model are the ATE,
There are also machine learning methods for estimating treatment effects with continuous confounders (e.g., double machine learning, REF , that are not interpretable.
M L ] 3 0 Ja n 20 17 Double / Debiased / Neyman Machine Learning of Treatment Effects by