Causal Machine Learning and Business Decision Making

Paul Hünermund, Jermain Christopher Kaminski, Carla Schmitt

Publikation: Bidrag til bog/antologi/rapportKonferenceabstrakt i proceedingsForskningpeer review

Abstrakt

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.
OriginalsprogEngelsk
TitelProceedings of the Eighty-first Annual Meeting of the Academy of Management
RedaktørerSonia Taneja
Antal sider1
UdgivelsesstedBriarcliff Manor, NY
ForlagAcademy of Management
Publikationsdato2021
DOI
StatusUdgivet - 2021
BegivenhedThe Academy of Management Annual Meeting 2021: Bringing the Manager Back in Management - Online
Varighed: 29 jul. 20214 aug. 2021
Konferencens nummer: 81
https://aom.org/events/annual-meeting

Konference

KonferenceThe Academy of Management Annual Meeting 2021
Nummer81
LokationOnline
Periode29/07/202104/08/2021
Internetadresse
NavnAcademy of Management Proceedings
ISSN2151-6561

Citationsformater