Determining causal relationships in leadership research using machine learning: the powerful synergy of experiments and data science

Abstract

Machine Learning (ML) techniques offer exciting new avenues for leadership research. In this paper we discuss how ML techniques can be used to inform predictive and causal models of leadership effects and clarify why both types of models are important for leadership research. We propose combining ML and experimental designs to draw causal inferences by introducing a recently developed technique to isolate “heterogeneous treatment effects.” We provide a step-by-step guide on how to design studies that combine field experiments with the application of ML to establish causal relationships with maximal predictive power. Drawing on examples in the leadership literature, we illustrate how the suggested approach can be applied to examine the impact of, for example, leadership behavior on follower outcomes. We also discuss how ML can be used to advance leadership research from theoretical, methodological and practical perspectives and consider limitations.