Operationalizing Longitudinal Causal Discovery Under Real-World Workflow Constraints
现实工作流程约束下的纵向因果发现AI 解读 ↓
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现实工作流程约束下的纵向因果发现
- 研究问题
- 机构工作流程形成的记录顺序与测量安排，如何约束纵向因果图空间，并减少同一时期变量方向的歧义？
- 方法与思路
- 作者编码流程导出的边约束、时间索引和块结构，结合纵向 LiNGAM 与自助法，估计滞后总效应及其不确定性。
- 材料陈述的贡献
- 摘要称，显式流程约束可改善结构解释；日本年度健康筛查队列应用展示了时间一致的子结构及滞后效应。
- 阅读关注点
- 应核对流程约束的来源与可信度、测量时间对齐方式，以及潜在混杂、混合变量和自助推断所需的识别条件。
- 阅读边界
- 这是 arXiv 预印本，同行评审及接收未核实；摘要未展开标准识别假设，流程一致性不能单独保证因果方向正确。
原文摘要
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 supporting intervention queries. In a nationwide annual health screening cohort in Japan with 107,261 individuals and 429,044 person-years, workflow-constrained longitudinal LiNGAM yields temporally consistent within-time substructures and interpretable lagged total effects with explicit uncertainty. Sensitivity analyses using alternative exposure and body-composition definitions preserve the main qualitative patterns. We argue that formalizing workflow-derived constraint classes improves structural interpretability without relying on domain-specific edge specification, providing a reproducible bridge between operational workflows and longitudinal causal discovery under standard identifiability assumptions.