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Double Machine Learning and Automated Confounder Selection -- A Cautionary Tale

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Listed:
  • Paul Hunermund

    (Copenhagen Business School)

  • Beyers Louw

    (Maastricht University)

  • Itamar Caspi

    (Bank of Israel)

Abstract

Double machine learning (DML) has become an increasingly popular tool for automated variable selection in high-dimensional settings. Even though the ability to deal with a large number of potential covariates can render selection-on-observables assumptions more plausible, there is at the same time a growing risk that endogenous variables are included, which would lead to the violation of conditional independence. This paper demonstrates that DML is very sensitive to the inclusion of only a few "bad controls" in the covariate space. The resulting bias varies with the nature of the theoretical causal model, which raises concerns about the feasibility of selecting control variables in a data-driven way.

Suggested Citation

  • Paul Hunermund & Beyers Louw & Itamar Caspi, 2021. "Double Machine Learning and Automated Confounder Selection -- A Cautionary Tale," Papers 2108.11294, arXiv.org, revised May 2023.
    • Handle: RePEc:arx:papers:2108.11294
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    1. Victor Chernozhukov & Carlos Cinelli & Whitney Newey & Amit Sharma & Vasilis Syrgkanis, 2021. "Long Story Short: Omitted Variable Bias in Causal Machine Learning," Papers 2112.13398, arXiv.org, revised May 2024.
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    Cited by:

    1. Patrick Rehill & Nicholas Biddle, 2023. "Fairness Implications of Heterogeneous Treatment Effect Estimation with Machine Learning Methods in Policy-making," Papers 2309.00805, arXiv.org.
    2. Santos, Anabela M. & Coad, Alex, 2023. "Monitoring and evaluation of transformative innovation policy: Suggestions for Improvement," Socio-Economic Planning Sciences, Elsevier, vol. 90(C).
    3. Bilgin, Rumeysa, 2023. "The Selection Of Control Variables In Capital Structure Research With Machine Learning," SocArXiv e26qf, Center for Open Science.

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