arXiv cs.AI· Alessandro Casadei, Sreyoshi Bhaduri, Rohit Malshe, Pavan Mullapudi, Raj Ratan, Ankush Pole, Arkajit Rakshit·· 4 小时前AI 评分13
arXiv 论文被撤稿：面向复杂系统根因分析的可扩展因果中介分析框架
Scaling Causal Mediation for Complex Systems: A Framework for Root Cause Analysis
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一篇提出可扩展因果中介分析框架的论文（arXiv:2512.14764），旨在将大型因果 DAG 中的总效应分解为可解释的直接与间接成分，并以履约中心物流为案例做根因分析。该论文已被 arXiv 管理员撤回，原因是未达到 arXiv 的研究内容质量标准。
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Abstract: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.
| Comments: | arXiv admin note: This submission has been withdrawn because it does not meet arXiv's research content quality standards |
| Subjects: | Methodology (stat.ME); Artificial Intelligence (cs.AI); Econometrics (econ.EM) |
| Cite as: | arXiv:2512.14764 [stat.ME] |
| (or arXiv:2512.14764v2 [stat.ME] for this version) | |
| https://doi.org/10.48550/arXiv.2512.14764 arXiv-issued DOI via DataCite |
Submission history
From: arXiv Admin [view email]
[v1]
Tue, 16 Dec 2025 04:06:40 UTC (23 KB)
[v2]
Thu, 29 Oct 2026 20:47:30 UTC (1 KB) (withdrawn)
来源：arXiv cs.AI · arxiv.org