Double Machine Learning and Automated Confounder Selection: A Cautionary Tale

Paul Hünermund; Beyers Louw; Itamar Caspi

doi:10.5465/AMBPP.2022.14311abstract

Double Machine Learning and Automated Confounder Selection: A Cautionary Tale

Paul Hünermund, Beyers Louw, Itamar Caspi

Institut for Strategi og Innovation

Publikation: Bidrag til bog/antologi/rapport › Konferenceabstrakt i proceedings › Forskning › peer review

Abstrakt

Double machine learning (DML) is becoming an increasingly popular tool for automated 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.

Originalsprog	Engelsk
Titel	Proceedings of the Eighty-second Annual Meeting of the Academy of Management
Redaktører	Sonia Taneja
Antal sider	1
Udgivelsessted	Briarcliff Manor, NY
Forlag	Academy of Management
Publikationsdato	2022
Sider	2199
DOI	https://doi.org/10.5465/AMBPP.2022.14311abstract
Status	Udgivet - 2022
Begivenhed	The Academy of Management Annual Meeting 2022: Creating a Better World Together - Seattle, USA Varighed: 5 aug. 2022 → 9 aug. 2022 Konferencens nummer: 82 https://2022.aom.org/

Konference

Konference	The Academy of Management Annual Meeting 2022
Nummer	82
Land/Område	USA
By	Seattle
Periode	05/08/2022 → 09/08/2022
Internetadresse	https://2022.aom.org/

Navn	Academy of Management Proceedings
ISSN	0065-0668

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10.5465/AMBPP.2022.14311abstractLicens: Ikke-specificeret

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Citationsformater

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abstract = "Double machine learning (DML) is becoming an increasingly popular tool for automated 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.",

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Hünermund, P, Louw, B & Caspi, I 2022, Double Machine Learning and Automated Confounder Selection: A Cautionary Tale. i S Taneja (red.), Proceedings of the Eighty-second Annual Meeting of the Academy of Management . Academy of Management, Briarcliff Manor, NY, Academy of Management Proceedings, s. 2199, The Academy of Management Annual Meeting 2022, Seattle, Washington, USA, 05/08/2022. https://doi.org/10.5465/AMBPP.2022.14311abstract

Double Machine Learning and Automated Confounder Selection : A Cautionary Tale. / Hünermund, Paul; Louw, Beyers; Caspi, Itamar.

Proceedings of the Eighty-second Annual Meeting of the Academy of Management . red. / Sonia Taneja. Briarcliff Manor, NY : Academy of Management, 2022. s. 2199 (Academy of Management Proceedings).

Publikation: Bidrag til bog/antologi/rapport › Konferenceabstrakt i proceedings › Forskning › peer review

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