Causal Machine Learning and Business Decision Making

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.