Causal inference for violence risk management and decision support in forensic psychiatry

• A Bayesian network model for violence risk analysis in psychotic patients.
• Proposes a causal framework for mental factors affecting violent behaviour.
• High predictive accuracy compared to other established models within this area.
• Allows for specific risk factors to be targeted for causal intervention.
• Interventions modelled allow for violence risk management.

The purpose of medium secure services (MSS) is to provide accommodation, support, and treatment to individuals with enduring mental health problems who usually come into contact with the criminal justice system. These individuals are, therefore, believed to pose a risk of violence to themselves as well as to other individuals. Assessing and managing the risk of violence is considered to be a critical component for discharged decision making in MSS. Methods for violence risk assessment in this area of research are typically based on regression models or checklists with no statistical composition and which naturally demonstrate mediocre predictive performance and, more importantly, without providing genuine decision support. While Bayesian networks have become popular tools for decision support in the medical field over the last couple of decades, they have not been extensively studied in forensic psychiatry. In this paper, we describe a decision support system using Bayesian networks, which is mainly parameterised based on questionnaire, interviewing and clinical assessment data, for violence risk assessment and risk management in patients discharged from MSS. The results demonstrate moderate to significant improvements in forecasting capability. More importantly, we demonstrate how decision support is improved over the well-established approaches in this area of research, primarily by incorporating causal interventions and taking advantage of the model's ability in answering complex probabilistic queries for unobserved variables.