TCRCA: Topology-Guided Causal Inference for Root Cause Analysis in Microservices

Microservice systems are widely adopted in modern cloud-native architectures due to their flexible modular design. However, complex inter-service dependencies and cascading fault propagation make root cause analysis (RCA) highly challenging. Although recent automated RCA methods have improved fault diagnosis from different perspectives, existing approaches still face difficulty in jointly achieving temporal causal validity, structural plausibility, and propagation-level interpretability. To address this limitation, we propose TCRCA, a topology-guided multi-modal causal inference framework designed for interpretable root cause localization in microservices. TCRCA constructs a unified multi-modal service graph that integrates logs, metrics, and traces, enabling more efficient fault propagation modeling. It introduces a multi-scale Granger causal mechanism, combined with service topology priors, to enhance causal reasoning and fault analysis. Experimental evaluations on three public benchmarks show that TCRCA significantly outperforms state-of-the-art methods, improving Top-1 root cause localization accuracy by 17.2%, reducing the number of false alerts by 69.6%, and accurately reconstructing over 92% of fault propagation paths. These results demonstrate the practical advantages of TCRCA in real-world applications.