Causal Machine Learning and Business Decision Making

64 Pages Posted: 24 Jun 2021

See all articles by Paul Hünermund

Paul Hünermund

Copenhagen Business School - Department of Strategy and Innovation

Jermain Kaminski

Maastricht University

Carla Schmitt

Maastricht University - School of Business and Economics

Date Written: May 18, 2021

Abstract

Causal knowledge is critical for strategic and organizational decision-making. By contrast, standard machine learning approaches remain purely correlational and prediction-based, rendering them unsuitable for addressing 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 senior decision-makers, as well as quantitative survey data, this study argues that causality is a critical boundary condition for the application of machine learning in a business analytical context. It highlights the crucial role of theory in causal inference and offers a new perspective on human-machine interaction for data-augmented decision making.

Keywords: Organizational Decision-making, Data Science, Causality, Machine Learning, Theory-based View

JEL Classification: C01, C45, D80, M21

Suggested Citation

Hünermund, Paul and Kaminski, Jermain and Schmitt, Carla, Causal Machine Learning and Business Decision Making (May 18, 2021). Available at SSRN: https://ssrn.com/abstract=3867326 or http://dx.doi.org/10.2139/ssrn.3867326

Paul Hünermund

Copenhagen Business School - Department of Strategy and Innovation ( email )

Kilen
Frederiksberg, 2000
Denmark

Jermain Kaminski (Contact Author)

Maastricht University ( email )

Tongersestraat 53
Maastricht, LM 6211
Netherlands

Carla Schmitt

Maastricht University - School of Business and Economics ( email )

Netherlands

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