Causal discovery methods in psychological research: Foundations, algorithms, and a practical tutorial in R

Journal article

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 computational techniques have introduced causal discovery methods as a powerful alternative. These methods can uncover complex causal network structures from observational or interventional data, enabling the identification of causal directions in intricate interdependencies involving numerous variables. Building on a growing body of literature, this paper provides a comprehensive survey of core causal discovery algorithms and their recent applications across various disciplines, with a particular focus on their use in uncovering psychological mechanisms. To complement this overview, we provide a tutorial using data from the Health Behavior in School-Aged Children (HBSC) study. This case study demonstrates how causal discovery can be applied to examine gender-specific mechanisms underlying bullying-related outcomes. We also discuss the opportunities and challenges of integrating causal discovery into psychological research.

Authors: Zhu, G; Tay, LQ; Zhang, M

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: Springer
• Journal: Behavior Research Methods
• Volume: 58
• Issue: 2
• Article number: 64
• Publication date: 2026-02-13
• Acceptance date: 2025-09-12
• DOI: 10.3758/s13428-025-02841-w
• EISSN: 1554-3528
• ISSN: 1554351X, 1554-351X
• Language: English
• Keywords: Causal structure learning; Psychological mechanism; Causal discovery; Bayesian network structure learning