An Introduction to Double/Debiased Machine Learning

Achim Ahrens, Victor Chernozhukov (), Christian Hansen (), Damian Kozbur (), Mark Schaffer () and Thomas Wiemann ()
Additional contact information
Victor Chernozhukov: Massachusetts Institute of Technology
Christian Hansen: University of Chicago
Damian Kozbur: University of Zurich
Thomas Wiemann: University of Chicago

No 18438, IZA Discussion Papers from IZA Network @ LISER

Abstract: This paper provides an introduction to Double/Debiased Machine Learning (DML). DML is a general approach to performing inference about a target parameter in the presence of nuisance functions: objects that are needed to identify the target parameter but are not of primary interest. Nuisance functions arise naturally in many settings, such as when controlling for confounding variables or leveraging instruments. The paper describes two biases that arise from nuisance function estimation and explains how DML alleviates these biases. Consequently, DML allows the use of flexible methods, including machine learning tools, for estimating nuisance functions, reducing the dependence on auxiliary functional form assumptions and enabling the use of complex non-tabular data, such as text or images. We illustrate the application of DML through simulations and empirical examples. We conclude with a discussion of recommended practices. A companion website includes additional examples and references to other resources.

Keywords: causal inference; econometrics; high-dimensional models; machine learning; nonparametric estimation (search for similar items in EconPapers)
JEL-codes: C14 C21 C23 C26 (search for similar items in EconPapers)
Date: 2026-03
New Economics Papers: this item is included in nep-cmp
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Working Paper: An Introduction to Double/Debiased Machine Learning (2026)
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