A Unified Approach to Interpretable Causal Root Cause Attribution

2026-08-30

AI Summary

本文提出一种结合结构因果信息的度量树分解框架，以解决电商系统中指标变化的根本原因归因问题，提高因果有效性同时保持可解释性和计算效率。

Abstract

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.

Problem

• root cause attribution
• interpretability
• causal validity
• e-commerce systems
• metric changes

Innovation

• Unified Approach
• Structural Causal Information
• Metric-Tree Decomposition
• Causal Validity
• Interpretability