提出可扩展的因果中介分析框架，用于复杂系统根因追溯。
Scaling Causal Mediation for Complex Systems: A Framework for Root Cause Analysis
- 基于大规模有向无环图分解总效应为直接与间接路径
- 在履约中心物流场景中成功定位被遮蔽的根源问题
- 适合处理多干预、多中介的高维复杂系统分析
从物流到云基础设施、工业物联网等现代运管系统，均受复杂互依流程支配。理解干预如何在系统中传播，需超越直接效应的因果推断方法，量化中介路径的影响。传统中介分析在简单场景有效，但难以扩展至实际中遇到的高维有向无环图（DAG），尤其当多个处理变量与中介变量交互时。本文提出一种面向多处理与多中介的可扩展中介分析框架，系统性地将总效应分解为可解释的直接与间接成分。通过履约中心物流中的应用案例，验证了其在复杂依赖关系与不可控因素下揭示根因的实际价值。
原文摘要 · Abstract (English)
Modern operational systems ranging from logistics and cloud infrastructure to industrial IoT, are governed by complex, interdependent processes. Understanding how interventions propagate through such systems requires causal inference methods that go beyond direct effects to quantify mediated pathways. Traditional mediation analysis, while effective in simple settings, fails to scale to the high-dimensional directed acyclic graphs (DAGs) encountered in practice, particularly when multiple treatments and mediators interact. In this paper, we propose a scalable mediation analysis framework tailored for large causal DAGs involving multiple treatments and mediators. Our approach systematically decomposes total effects into interpretable direct and indirect components. We demonstrate its practical utility through applied case studies in fulfillment center logistics, where complex dependencies and non-controllable factors often obscure root causes.
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