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

Department of Strategy and Innovation

Research output: Chapter in Book/Report/Conference proceeding › Conference abstract in proceedings › Research › peer-review

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.

Original language	English
Title of host publication	Proceedings of the Eighty-second Annual Meeting of the Academy of Management
Editors	Sonia Taneja
Number of pages	1
Place of Publication	Briarcliff Manor, NY
Publisher	Academy of Management
Publication date	2022
Pages	2199
DOIs	https://doi.org/10.5465/AMBPP.2022.14311abstract
Publication status	Published - 2022
Event	The Academy of Management Annual Meeting 2022: Creating a Better World Together - Seattle, United States Duration: 5 Aug 2022 → 9 Aug 2022 Conference number: 82 https://2022.aom.org/

Conference

Conference	The Academy of Management Annual Meeting 2022
Number	82
Country/Territory	United States
City	Seattle
Period	05/08/2022 → 09/08/2022
Internet address	https://2022.aom.org/

Series	Academy of Management Proceedings
ISSN	0065-0668

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Cite this

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Hünermund, P, Louw, B & Caspi, I 2022, Double Machine Learning and Automated Confounder Selection: A Cautionary Tale. in S Taneja (ed.), Proceedings of the Eighty-second Annual Meeting of the Academy of Management . Academy of Management, Briarcliff Manor, NY, Academy of Management Proceedings, pp. 2199, The Academy of Management Annual Meeting 2022, Seattle, Washington, United States, 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 . ed. / Sonia Taneja. Briarcliff Manor, NY: Academy of Management, 2022. p. 2199 (Academy of Management Proceedings).

Research output: Chapter in Book/Report/Conference proceeding › Conference abstract in proceedings › Research › peer-review

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