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 ...
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.
