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A Unified Approach to Interpretable Causal Root Cause Attribution — arXiv:2608.29735

Source: arXiv (en)
Original: https://arxiv.org/abs/2608.29735
License (as archived): CC0-1.0
Archived: 2026-10-17T08:59:57.323Z
SHA-256 of the archived text: 8fe45bb21aa6f0ce64dae10ba6cc2fd8921b1ebca602127bbde5942977c9b896
Stable URL of this page: https://metraipolis.com/documento/dc_cd26f341bc15

How to cite

A Unified Approach to Interpretable Causal Root Cause Attribution — arXiv:2608.29735 — arXiv (en), https://arxiv.org/abs/2608.29735 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_cd26f341bc15 (Metraipolis archive dc_cd26f341bc15, sha256 8fe45bb21aa6f0ce64dae10ba6cc2fd8921b1ebca602127bbde5942977c9b896)

BibTeX

@misc{dccd26f341bc15,
  title = {A Unified Approach to Interpretable Causal Root Cause Attribution — arXiv:2608.29735},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2608.29735},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_cd26f341bc15; sha256 8fe45bb21aa6f0ce64dae10ba6cc2fd8921b1ebca602127bbde5942977c9b896},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2608.29735. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Understanding why a target metric changes is a fundamental problem in data-driven decision making, beyond anomaly detection alone. We study root cause attribution for metric changes in complex e-commerce systems, focusing on trade-offs between interpretability, efficiency, and causal validity. As a starting point, we extend a metric-decomposition method into a recursive metric-tree framework for multi-level root cause analysis, but this relies on independence and decomposability assumptions that miss complex causal dependencies. In contrast, graphical causal models (GCMs) relax these assumptions and improve causal validity, at the cost of interpretability, higher computational and data demands, and potential attribution target misalignment. Through real-world applications, mathematical proofs, and simulations, we characterize the fundamental sources of these trade-offs. Guided by these insights, we propose a unified, causally informed attribution approach that integrates structural causal information into the metric-tree decomposition framework and corrects key sources of misalignment in GCM-based causal attributions, substantially improving causal validity while preserving interpretability and fast computation. Analytical proofs and simulations demonstrate that the proposed approach produces more accurate root cause attributions, and we also present a real-world application.

Tema degli esploratori: statistica e scienza dei dati (retro-catalogo arXiv: annunci del 2026-09-01 in stat.ME).
Categorie arXiv: Methodology (stat.ME) ; Applications (stat.AP).
Pagina del lavoro: https://arxiv.org/abs/2608.29735 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-17T08:39:34.518Z dalla pagina https://arxiv.org/catchup/stat.ME/2026-09-01?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).