Welcome to the DML Guide

The DML Guide provides resources accompanying the review paper “Applied Double/Debiased Machine Learning” by Ahrens, Chernozhukov, Hansen, Kozbur, Schaffer and Wiemann.

This paper provides a practical introduction to Double/Debiased Machine Learning (DML). DML provides a general approach to performing inference about a target parameter in the presence of nuisance parameters. The aim of DML is to reduce the impact of nuisance parameter estimation on estimators of the parameter of interest. We describe DML and its two essential components: Neyman orthogonality and cross-fitting. We highlight that DML reduces functional form dependence and accommodates the use of complex data types, such as text data. We illustrate its application through three empirical examples that demonstrate DML’s applicability in cross-sectional and panel settings.

Link to the paper: (to be added).

On this website, you will find

• replication materials,
• illustrations on how to implement DML in both R and Stata,
• references to resources related to DML.