A Complex Systems Approach to Causal Discovery in Psychiatry

Area of Science:

• Psychiatric Research
• Computational Psychiatry
• Systems Science

Background:

• Traditional psychiatric research struggles to infer causality and understand complex systems.
• Existing methods lack the ability to reliably identify causal relationships in psychiatric disorders without experimental designs.

Purpose of the Study:

• To validate a novel hybrid computational approach, the Complex Systems-Causal Network (CS-CN) method.
• To integrate causal discovery within a complex systems framework for psychiatric research.
• To identify risk factors and potential treatments for psychiatric disorders.

Main Methods:

• The Complex Systems-Causal Network (CS-CN) method was applied to datasets of hospitalized and traumatized children.
• A resimulation controlled experiment using a 'gold standard' dataset compared CS-CN with other causal discovery methods.
• The method was validated through application to existing and larger datasets, and a controlled experiment.

Main Results:

• The CS-CN method successfully detected a causal network of 111 variables and 167 relations in an initial study.
• Key variables disproportionally contributing to network adaptive properties were identified, with their removal causing significant loss of these properties.
• CS-CN outperformed traditional methods and performed comparably to state-of-the-art algorithms in causal detection.

Conclusions:

• The CS-CN method is a validated and replicated approach for psychiatric research.
• It provides novel and previously validated findings on risk factors and potential treatments.
• This method offers both fine-grain (micro) and high-level (macro) insights for complex systems-oriented research.