Causal Inference in Strategy: How can Machine Learning Help?

Abstract

Recent advances in machine learning methods have opened up a set of new possibilities for inductive theorizing, and for analysis of unstructured text data in strategy and organizations research. In contrast, the application of machine learning to improve causal explanation in deductive strategy research is less well understood. In this paper, we spotlight supervised machine learning and its application to matching methods to support stronger inference in deductive research. We use a simulation and an analysis of technology invention data to illustrate the method. A core contribution is to provide guidance to strategy researchers in the use of supervised machine learning.