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

Double machine learning (DML) is becoming an increasingly popular tool for automatic model selection in high-dimensional settings. These approaches rely on the assumption of conditional independence, which may not hold in big-data settings where the covariate space is large. This paper shows that DM...

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Main Authors: Hünermund, Paul, Louw, Beyers, Caspi, Itamar
Format: Text
Language:unknown
Published: 2021
Subjects:
stat
psy
DML
Online Access:http://arxiv.org/abs/2108.11294
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spelling fttriple:oai:gotriple.eu:10670/1.n5q8jt 2023-05-15T16:01:23+02:00 Double Machine Learning and Automated Confounder Selection -- A Cautionary Tale Hünermund, Paul Louw, Beyers Caspi, Itamar 2021-08-25 http://arxiv.org/abs/2108.11294 undefined unknown 10670/1.n5q8jt http://arxiv.org/abs/2108.11294 undefined arXiv stat psy Text https://vocabularies.coar-repositories.org/resource_types/c_18cf/ 2021 fttriple 2023-01-22T18:13:57Z Double machine learning (DML) is becoming an increasingly popular tool for automatic model selection in high-dimensional settings. These approaches rely on the assumption of conditional independence, which may not hold in big-data settings where the covariate space is large. This paper shows that DML is very sensitive to the inclusion of even a few "bad controls" in the covariate space. The resulting bias varies with the nature of the causal model, which raises concerns about the feasibility of selecting control variables in a data-driven way. Text DML Unknown
institution Open Polar
collection Unknown
op_collection_id fttriple
language unknown
topic stat
psy
spellingShingle stat
psy
Hünermund, Paul
Louw, Beyers
Caspi, Itamar
Double Machine Learning and Automated Confounder Selection -- A Cautionary Tale
topic_facet stat
psy
description Double machine learning (DML) is becoming an increasingly popular tool for automatic model selection in high-dimensional settings. These approaches rely on the assumption of conditional independence, which may not hold in big-data settings where the covariate space is large. This paper shows that DML is very sensitive to the inclusion of even a few "bad controls" in the covariate space. The resulting bias varies with the nature of the causal model, which raises concerns about the feasibility of selecting control variables in a data-driven way.
format Text
author Hünermund, Paul
Louw, Beyers
Caspi, Itamar
author_facet Hünermund, Paul
Louw, Beyers
Caspi, Itamar
author_sort Hünermund, Paul
title Double Machine Learning and Automated Confounder Selection -- A Cautionary Tale
title_short Double Machine Learning and Automated Confounder Selection -- A Cautionary Tale
title_full Double Machine Learning and Automated Confounder Selection -- A Cautionary Tale
title_fullStr Double Machine Learning and Automated Confounder Selection -- A Cautionary Tale
title_full_unstemmed Double Machine Learning and Automated Confounder Selection -- A Cautionary Tale
title_sort double machine learning and automated confounder selection -- a cautionary tale
publishDate 2021
url http://arxiv.org/abs/2108.11294
genre DML
genre_facet DML
op_source arXiv
op_relation 10670/1.n5q8jt
http://arxiv.org/abs/2108.11294
op_rights undefined
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