Double Machine Learning and Automated Confounder Selection: A Cautionary Tale

Paul Hünermund, Beyers Louw, Itamar Caspi

Publikation: Bidrag til bog/antologi/rapportKonferenceabstrakt i proceedingsForskningpeer 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.
OriginalsprogEngelsk
TitelProceedings of the Eighty-second Annual Meeting of the Academy of Management
RedaktørerSonia Taneja
Antal sider1
UdgivelsesstedBriarcliff Manor, NY
ForlagAcademy of Management
Publikationsdato2022
Sider2199
DOI
StatusUdgivet - 2022
BegivenhedThe Academy of Management Annual Meeting 2022: Creating a Better World Together - Seattle, USA
Varighed: 5 aug. 20229 aug. 2022
Konferencens nummer: 82
https://2022.aom.org/

Konference

KonferenceThe Academy of Management Annual Meeting 2022
Nummer82
Land/OmrådeUSA
BySeattle
Periode05/08/202209/08/2022
Internetadresse
NavnAcademy of Management Proceedings
ISSN0065-0668

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