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 model 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.

机器学习 (ML) 技术为领导力研究提供了令人兴奋的新途径。在本文中，我们讨论了如何使用 ML 技术为领导力效应的预测模型和因果模型提供信息，并阐明为什么这两种模型对领导力研究都很重要。我们建议将 ML 和实验设计结合起来，通过引入最近开发的技术来分离“异质治疗效果”，从而得出因果推论。我们提供了有关如何设计研究的分步指南，这些研究将现场实验与 ML 的应用相结合，以建立具有最大预测能力的因果关系。借鉴领导力文献中的例子，我们说明了如何应用建议的方法来检查领导行为对追随者结果的影响。