Double/Debiased/Neyman Machine Learning of Treatment Effects
This is an incremental application of an existing method to a specific domain (causal inference with observational data).
The paper tackles the problem of estimating average treatment effects from observational data by applying a double/de-biased machine learning method, which uses Neyman-orthogonal scores and cross-fitting to provide valid inference when nuisance parameters are estimated with high-dimensional nonparametric techniques.
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).