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

Area of Science:

• Psychology
• Computational Methods
• Causal Inference

Background:

• Understanding psychological causality is crucial for theory and interventions.
• Traditional methods like experiments and SEM have limitations in complex systems.
• Causal discovery offers advanced computational approaches for uncovering causal networks from data.

Purpose of the Study:

• To provide a comprehensive survey of core causal discovery algorithms.
• To highlight recent applications of causal discovery across disciplines, focusing on psychological mechanisms.
• To demonstrate the practical application of causal discovery in psychological research through a case study.

Main Methods:

• Review of core causal discovery algorithms.
• Analysis of observational and interventional data using computational techniques.
• Case study utilizing data from the Health Behavior in School-Aged Children (HBSC) study.

Main Results:

• Causal discovery methods can uncover complex causal network structures and identify causal directions.
• The HBSC case study illustrates gender-specific mechanisms underlying bullying-related outcomes.
• The paper discusses the potential and limitations of integrating causal discovery into psychological research.

Conclusions:

• Causal discovery is a powerful tool for advancing the understanding of psychological mechanisms.
• The integration of causal discovery methods offers new avenues for psychological research and intervention design.
• Further research is needed to address the challenges and fully leverage the opportunities of causal discovery in psychology.