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

Paul H\"unermund, Beyers Louw and Itamar Caspi
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Paul H\"unermund: Copenhagen Business School
Beyers Louw: Maastricht University
Authors registered in the RePEc Author Service: Paul Hünermund

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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.

Date: 2021-08, Revised 2023-05
New Economics Papers: this item is included in nep-big, nep-cmp, nep-ecm and nep-isf
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Citations: View citations in EconPapers (3)

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Journal Article: Double machine learning and automated confounder selection: A cautionary tale (2023)
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Persistent link: https://EconPapers.repec.org/RePEc:arx:papers:2108.11294

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