  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).
