Causal Discovery in Strategic Management Research



In-Person in T09-67 and online via Zoom (

Abstract. In recent years, management scholars have developed a keen interest in machine learn- ing as a toolkit for pattern discovery in empirical research (Choudhury et al., 2020). A drawback of these methods, however, is that they are generally not able to infer causal relationships from data, which poses severe limitations for theory development and testing (Hünermund et al., 2021). In this project, we introduce a novel variant of machine learning algorithms from the causal AI literature (Spirtes et al., 2000; Peters et al., 2017) that are able to overcome this limitation. These causal discovery algorithms leverage testable constraints imposed by the data generating process, to infer the causal structure compatible with the observed conditional independence relationships in the data in a purely autonomous and data-driven way. We discuss the strengths and weaknesses of this approach for research in strategic and innovation management and present an application to the determinants of AI adoption in U.S. publicly listed firms.



Choudhury, P., Allen, R., and Endres, M. G. (2020). Machine learning for pattern discovery in management research. Strategic Management Journal, 42(1):30–57. 

Hünermund, P., Kaminski, J., and Schmitt, C. (2021). Causal machine learning and business decision making. Available at SSRN: 

Spirtes, P., Glymour, C., and Scheines, R. (2000). Causation, Prediction, and Search. The MIT Press, Cambride, MA, 2nd edition. 

Peters, J., Janzing, D., and Sch ̈olkopf, B. (2017). Elements of Causal Inference. The MIT Press.