TY  - RPRT
        AU  - Ahrens, Achim
AU  - Chernozhukov, Victor
AU  - Hansen, Christian
AU  - Kozbur, Damian
AU  - Schaffer, Mark E
AU  - Wiemann, Thomas
        TI  - An Introduction to Double/Debiased Machine Learning
        PY  - 2026/Mar/
        PB  - Institute of Labor Economics (IZA)
        CY  - Bonn
        T2  - IZA Discussion Paper
        IS  - 18438
        UR  - https://www.iza.org/publications/dp18438
        AB  - 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.
        KW  - causal inference
KW  - econometrics
KW  - high-dimensional models
KW  - machine learning
KW  - nonparametric estimation
        ER  -