Necessity causality in mental health research: Applying necessary condition analysis in clinical psychology and psychiatry

Understanding the causal mechanisms underlying the development of mental disorders and their symptoms is essential for advancing effective prevention and treatment strategies. However, research in this field has predominantly relied on sufficiency logic within a probabilistic framework, coupled with traditional statistical methods (i.e., multiple linear regression, Structural Equation Modelling, etc.) where risk factors are associated with an increased likelihood of developing a disorder. While valuable, this approach also carries inherent assumptions and limitations. Additionally, the crucial concept of causal necessity has been largely overlooked. By integrating necessity logic within a deterministic framework—where the absence of a necessary risk factor prevents the development of a disorder in nearly everyone— we propose a novel and promising approach, exemplified by Necessary Condition Analysis (NCA). In this paper, we outline the theoretical foundations of NCA and illustrate its potential for advancing mental health research, with a specific application to the Interpersonal Theory of Suicide. We also discuss how NCA can address critical challenges in mental health science, refine existing methodologies, and open new pathways for enhancing both research and clinical practice.