可解释性因果根因归因的统一方法
A Unified Approach to Interpretable Causal Root Cause Attribution
- Amazon（亚马逊）
机构由 AI 辅助整理，请以论文原文为准。
中文总结 AI 辅助
针对复杂电商系统的指标变化根因归因，提出结合结构因果信息的统一方法，兼顾可解释性、效率与因果有效性，提升了根因归因的准确性。
中文摘要 AI 辅助
理解目标指标变化的原因是数据驱动决策中的基础问题，其重要性远超异常检测本身。我们针对复杂电商系统中指标变化的根因归因展开研究，重点关注可解释性、效率与因果有效性三者间的权衡。作为起点，我们将指标分解方法扩展为用于多级根因分析的递归指标树框架，但该方法依赖独立性与可分解性假设，无法捕捉复杂的因果依赖关系。相比之下，图形因果模型（GCM）放宽了这些假设，提升了因果有效性，但需以可解释性下降、计算与数据需求更高、归因目标可能错位为代价。通过实际应用、数学证明与模拟，我们明确了这些权衡的根本来源。基于这些见解，我们提出一种统一的、结合因果知识的归因方法，将结构因果信息融入指标树分解框架，并修正了基于GCM的因果归因中关键的错位来源，在保持可解释性与快速计算的同时大幅提升了因果有效性。分析证明与模拟显示，所提方法能生成更准确的根因归因，我们还展示了其在实际场景中的应用。
英文摘要
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.
