Causal Discovery Maps Observational Medical Research

A scoping review published in JMIR Medical Informatics in 2026 systematically maps applications of machine-learning causal discovery methods in observational medical research, searching six databases through May 2025 and including 72 eligible studies from 2,296 records. The review finds constraint-based algorithms most common (53%), predominant clinical applications in mental health and chronic disease, and recurring challenges such as unmeasured confounding, limited sample sizes, and lack of standardized validation frameworks.