Causal Discovery Methods in Psychological Research: A Tutorial in R

Understanding causality and the mechanisms underlying psychological phenomena has been a cornerstone of psychological research, with significant implications for theory development and intervention design. While traditional methods such as experimental manipulations or structural equation modelling have been extensively used to explore causal relationships, recent advances in causal inference studies have introduced causal discovery methods as a powerful alternative. These methods can uncover complex causal structures from observational or interventional data, offering unique utility such as identifying causal directions and handling complex causal networks involving numerous variables. This paper provides an introduction to causal discovery methods tailored for psychological research. We review foundational concepts, introduce the classical algorithms, and present a practical tutorial in R, equipping researchers with tools to implement these methods on psychological data. We also discuss the potential of these methods to accelerate theory formation, refine interventions, and foster cross-disciplinary understanding of human cognition as well as the challenges of integrating causal discovery into psychological research.