Operationalizing Longitudinal Causal Discovery Under Real-World Workflow Constraints

Researchers developed a framework for causal discovery in longitudinal data systems that addresses real-world workflow constraints by incorporating institutional protocols and timeline structures. The method was tested on a large Japanese health screening dataset with over 100,000 individuals, showing improved structural interpretability without requiring domain-specific specifications.