# Double/Debiased/Neyman Machine Learning of Treatment Effects

## TL;DR

This paper discusses a double/debiased machine learning approach for valid inference on treatment effects, utilizing Neyman-orthogonal scores and cross-fitting, especially in high-dimensional observational data.

## Contribution

It applies the double/debiased machine learning method to estimate average treatment effects and effects on the treated, demonstrating its practical use in observational studies.

## Key findings

- Effective estimation of ATE and ATTE using high-dimensional data.
- Validation of the double/debiased ML approach for treatment effect inference.
- Enhanced accuracy in treatment effect estimation with modern machine learning methods.

## Abstract

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).