Double machine learning and automated confounder selection: A cautionary tale

Paul Hünermund, Louw Beyers () and Itamar Caspi
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Louw Beyers: Maastricht University, Tongersestraat 53, 6211 LM Maastricht, Netherlands

Journal of Causal Inference, 2023, vol. 11, issue 1, 12

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 article 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.

Keywords: double/debiased machine learning; bad controls; backdoor adjustment; collider bias; causal hierarchy (search for similar items in EconPapers)
Date: 2023
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Citations: View citations in EconPapers (1)

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Working Paper: Double Machine Learning and Automated Confounder Selection -- A Cautionary Tale (2023)
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Persistent link: https://EconPapers.repec.org/RePEc:bpj:causin:v:11:y:2023:i:1:p:12:n:1

DOI: 10.1515/jci-2022-0078

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