Causal Machine Learning and Business Decision Making

Paul Hünermund, Jermain Christopher Kaminski, Carla Schmitt

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Abstract

Causal knowledge is critical for strategic and organizational decision making. By contrast, standard machine learning approaches remain purely pattern and prediction-based, rendering them unsuitable for being applied to a wide variety of managerial decision problems. Taking a mixed-methods approach, which relies on multiple sources, including semi-structured interviews with data scientists and decision makers, as well as quantitative survey data, this study makes a first attempt at delineating causality as a critical boundary condition for the application of machine learning in business. It highlights the crucial role of theory in causal inference and offers a new perspective on human-machine interaction for data-augmented decision making.
Original languageEnglish
Title of host publicationProceedings of the Eighty-first Annual Meeting of the Academy of Management
EditorsSonia Taneja
Number of pages1
Place of PublicationBriarcliff Manor, NY
PublisherAcademy of Management
Publication date2021
DOIs
Publication statusPublished - 2021
EventThe Academy of Management Annual Meeting 2021: Bringing the Manager Back in Management - Online
Duration: 29 Jul 20214 Aug 2021
Conference number: 81
https://aom.org/events/annual-meeting

Conference

ConferenceThe Academy of Management Annual Meeting 2021
Number81
LocationOnline
Period29/07/202104/08/2021
Internet address
SeriesAcademy of Management Proceedings
ISSN2151-6561

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