Graph-Free Root Cause Analysis

2026-01-29

AI Summary

This work addresses the limitations of traditional root cause analysis methods in complex systems lacking dependency graphs, where such approaches often erroneously identify components with high anomaly scores as root causes—particularly when fault propagation amplifies anomalies downstream. To overcome this challenge, the authors propose PRISM, a novel framework that, for the first time, offers theoretically grounded root cause localization without requiring a dependency graph. PRISM leverages a component-based system model and the intrinsic properties of anomaly propagation to construct a lightweight inference algorithm capable of identifying true root causes in the absence of topological information. Evaluated on 735 real-world faults across nine datasets, PRISM achieves a Top-1 accuracy of 68%, representing a 258% improvement over the best baseline method, while requiring only 8 milliseconds per diagnosis.

Technology Category

• Reasoning under Uncertainty: Causality
• Knowledge Representation and Reasoning: Diagnosis and Abductive Reasoning
• Machine Learning: Causal Learning

Application Category

• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Failures in complex systems demand rapid Root Cause Analysis (RCA) to prevent cascading damage. Existing RCA methods that operate without dependency graph typically assume that the root cause having the highest anomaly score. This assumption fails when faults propagate, as a small delay at the root cause can accumulate into a much larger anomaly downstream. In this paper, we propose PRISM, a simple and efficient framework for RCA when the dependency graph is absent. We formulate a class of component-based systems under which PRISM performs RCA with theoretical guarantees. On 735 failures across 9 real-world datasets, PRISM achieves 68% Top-1 accuracy, a 258% improvement over the best baseline, while requiring only 8ms per diagnosis.

Problem

• Root Cause Analysis
• dependency graph
• fault propagation
• anomaly score
• component-based systems

Innovation

• Root Cause Analysis
• Graph-Free RCA
• PRISM
• Anomaly Propagation
• Component-Based Systems