An Introduction to Double/Debiased Machine Learning

• URL: http://arxiv.org/abs/2504.08324v1
• Date: Fri, 11 Apr 2025 07:48:42 GMT
• Title: An Introduction to Double/Debiased Machine Learning
• Authors: Achim Ahrens, Victor Chernozhukov, Christian Hansen, Damian Kozbur, Mark Schaffer, Thomas Wiemann,
• Abstract summary: This paper provides a practical introduction to Double/Debiased Machine Learning (DML)<n>We describe DML and its two essential components: Neymanity and cross-fitting.<n>We illustrate its application through three empirical examples that demonstrate DML's applicability in cross-sectional and panel settings.
• Score: 6.847418094278082
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: 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.

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