Causelens: Causality-Based Interpretable Root Cause Analysis for Microservice Systems
Qihan Liu, Pengfei Chen, Guangba Yu, Yuanhao Lai, Xiaoyun Li · 2025
Microservice applications consist of complex API invocation relationships, where a single fault can propagate through multiple paths, leading to widespread failures. The diverse propagation patterns of different faults make efficient and interpretable root cause analysis (RCA) crucial. We propose CauseLens, a causality-based unsupervised RCA framework that improves both accuracy and interpretability. The key insight is that fine-grained causal modeling enhances root cause localization. CauseLens constructs a heterogeneous causal diagram at the operation and entity levels using normal monitoring data (i.e., metrics and traces) and trains a structural causal model. It then integrates reconstruction error and counterfactual analysis to identify root causes while revealing fault propagation paths. Experiments on two microservice datasets demonstrate that CauseLens outperforms state-of-the-art methods in RCA accuracy. Further ablation studies and parameter experiments validate its design, while overhead analysis confirms its feasibility for real-time RCA in production environments.