Double machine learning and automated confounder selection: A cautionary tale

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...

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Bibliographic Details
Published in:Journal of Causal Inference
Main Authors: Hünermund Paul, Louw Beyers, Caspi Itamar
Format: Article in Journal/Newspaper
Language:English
Published: De Gruyter 2023
Subjects:
double/debiased machine learning
bad controls
backdoor adjustment
collider bias
causal hierarchy
62d20
Mathematics
QA1-939
Probabilities. Mathematical statistics
QA273-280
DML
Online Access:https://doi.org/10.1515/jci-2022-0078
https://doaj.org/article/6ee6a807d4aa4e5581151710701dcbd0
Description
Summary: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.

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