[2602.23800] Operationalizing Longitudinal Causal Discovery Under Real-World Workflow Constraints

Statistics > Methodology arXiv:2602.23800 (stat) [Submitted on 27 Feb 2026] Title:Operationalizing Longitudinal Causal Discovery Under Real-World Workflow Constraints Authors:Tadahisa Okuda, Shohei Shimizu, Thong Pham, Tatsuyoshi Ikenoue, Shingo Fukuma View a PDF of the paper titled Operationalizing Longitudinal Causal Discovery Under Real-World Workflow Constraints, by Tadahisa Okuda and 4 other authors View PDF HTML (experimental) Abstract:Causal discovery has achieved substantial theoretical progress, yet its deployment in large-scale longitudinal systems remains limited. A key obstacle is that operational data are generated under institutional workflows whose induced partial orders are rarely formalized, enlarging the admissible graph space in ways inconsistent with the recording process. We characterize a workflow-induced constraint class for longitudinal causal discovery that restricts the admissible directed acyclic graph space through protocol-derived structural masks and timeline-aligned indexing. Rather than introducing a new optimization algorithm, we show that explicitly encoding workflow-consistent partial orders reduces structural ambiguity, especially in mixed discrete--continuous panels where within-time orientation is weakly identified. The framework combines workflow-derived admissible-edge constraints, measurement-aligned time indexing and block structure, bootstrap-based uncertainty quantification for lagged total effects, and a dynamic representation s...