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.

• →A new framework addresses the gap between theoretical causal discovery progress and practical deployment in large-scale longitudinal systems.
• →The approach uses workflow-derived constraints and timeline-aligned indexing to reduce structural ambiguity in mixed discrete-continuous data panels.
• →Testing on a Japanese health cohort with 107,261 individuals demonstrated temporally consistent results with explicit uncertainty quantification.
• →The method improves structural interpretability without relying on domain-specific edge specifications.
• →The framework provides a reproducible bridge between operational workflows and longitudinal causal discovery under standard assumptions.