Mental disorders are highly prevalent, and among the leading causes of global disease burden. To respond in a timely and effective manner, a strong understanding of the structure of psychopathology and its development is critical. We compared the ability of two competing frameworks, the dynamic mutualism theory and the common cause theory, to explain the development of individual differences in psychopathology. We formalized these theories into statistical models, and applied them to two domains of psychopathology, at two different developmental periods, using two large developmental cohorts: the p factor (i.e. general psychopathology) from early to late adolescence (N = 1,482), and major depressive disorder in middle adulthood and old age (N = 6,443). The development of the p factor was better explained by a mutualistic account. In contrast, the evidence for the development of major depression was more ambiguous. Our results support a multicausal approach to understanding psychopathology and showcase the value of translating theories into testable statistical models for understanding developmental processes in clinical sciences.

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Mental disorders are among the leading causes of global disease burden. To respond effectively, a strong understanding of the structure of psychopathology is critical. We empirically compared two competing frameworks, dynamic-mutualism theory and common-cause theory, that vie to explain the development of psychopathology. We formalized these theories in statistical models and applied them to explain change in the general factor of psychopathology (p factor) from early to late adolescence ( N = 1,482) and major depression in middle adulthood and old age ( N = 6,443). Change in the p factor was better explained by mutualism according to model-fit indices. However, a core prediction of mutualism was not supported (i.e., predominantly positive causal interactions among distinct domains). The evidence for change in depression was more ambiguous. Our results support a multicausal approach to understanding psychopathology and showcase the value of translating theories into testable statistical models for understanding developmental processes in clinical sciences.