A Complex Systems Approach to Causal Discovery in Psychiatry

Abstract

Conventional research methodologies and data analytic approaches in psychiatric research are unable to reliably infer causal relations without experimental designs, or to make inferences about the functional properties of the complex systems in which psychiatric disorders are embedded. This article describes a series of studies to validate a novel hybrid computational approach--the Complex Systems-Causal Network (CS-CN) method-designed to integrate causal discovery within a complex systems framework for psychiatric research. The CS-CN method was first applied to an existing dataset on psychopathology in 163 children hospitalized with injuries (validation study). Next, it was applied to a much larger dataset of traumatized children (replication study). Finally, the CS-CN method was applied in a controlled experiment using a 'gold standard' dataset for causal discovery and compared with other methods for accurately detecting causal variables (resimulation controlled experiment). The CS-CN method successfully detected a causal network of 111 variables and 167 bivariate relations in the initial validation study. This causal network had well-defined adaptive properties and a set of variables was found that disproportionally contributed to these properties. Modeling the removal of these variables resulted in significant loss of adaptive properties. The CS-CN method was successfully applied in the replication study and performed better than traditional statistical methods, and similarly to state-of-the-art causal discovery algorithms in the causal detection experiment. The CS-CN method was validated, replicated, and yielded both novel and previously validated findings related to risk factors and potential treatments of psychiatric disorders. The novel approach yields both fine-grain (micro) and high-level (macro) insights and thus represents a promising approach for complex systems-oriented research in psychiatry.

Fig 1. The Complex Systems-Causal Network (CS-CN) Method.

Fig 2. In-degree Distribution of the CHIDS Network (logarithmic scale).

Fig 3. Out-degree Distribution of the CHIDS Network (logarithmic scale).

Fig 4. Distribution of Shortest Paths in the CHIDS Network and the Random Network.

Fig 5. The CHIDS Network and its Eight Modules.

The 15 highest ranked nodes based on BC score are indicated by numeric rank order.

Fig 6. Integrity of CHIDS Causal Network Following Challenge.

The proportion of nodes in the largest network component by sequential removal of 15 nodes at random vs. by BC rank.

Fig 7. The CHIDS Causal Network After Random Node Removal.

The CHIDS network after the sequential removal of 15 nodes at random.

Fig 8. The CHIDS Causal Network After Node Removal by BC Rank.

The CHIDS network after the sequential removal of 15 nodes by BC rank.