Judgments about a client's behavior problems and treatment goals, and the factors that influence them, are elements of most clinical case formulations (CCFs). These judgments are designed to guide clinicians' selection of the most effective intervention foci. Despite their importance, CCFs have undergone infrequent psychometric evaluations. We describe a model to promote and facilitate the psychometric evaluation of CCFs with quantified causal diagrams. This article presents the conceptual foundations, path analyses, benefits, and limitations of quantified causal diagrams. We first present concepts of causality and causal diagrams that are applicable to CCF and psychopathology. We propose that clinical case formulations causal diagrams can strengthen a science-based approach to clinical assessment, facilitate the psychometric evaluation of CCFs, enhance the specificity, precision, and communicability of clinicians' judgments, help the clinician select the most effective intervention foci, predict the effects of changes in causal variables, and emphasize the importance of "uncertainty" in CCFs.
Public Significance StatementClinical case formulations often guide the selection of the best treatment focus and strategy for a patient but have undergone infrequent evaluation of their reliability and validity. This article advances the idea that quantified causal diagrams of clinical case formulations can facilitate their psychometric evaluation and illustrate their assets and limitations.