Root Cause or Delayed Symptom? Lag-Calibrated Counterfactual Inference for Microservice Fault Localization

In microservice systems, fault propagation often involves non-negligible time lag, and faults in upstream services may manifest as anomalies in downstream services only after several time steps. Therefore, relying solely on anomaly signals observed at the current timestamp often makes it difficult to distinguish the true upstream root cause from downstream delayed response services. Most approaches rely on anomaly intensity or statistical correlations at individual timestamps, without explicitly modeling propagation delays or temporal causal relationships. As a result, they are prone to errors when anomaly signals are not synchronized across services. To address this issue, we propose RippleRCA, a hierarchical lag-calibrated causal inference framework for root cause analysis in microservice systems. Specifically, RippleRCA begins by capturing global cross-service propagation dependencies through edge-level and node-level lag-aware causal priors constructed with PCMCI+ under service call topology constraints. It then performs coarse-grained candidate recall from both the structural anomaly and propagation response perspectives. This is achieved through a dual-channel mechanism based on the features of temporal difference. Furthermore, RippleRCA further performs lag-calibrated temporal alignment and estimates counterfactual effects via recovery interventions, enabling fine-grained causal root cause ranking. Extensive experiments on two public datasets, AIOps2025 and RCABench, demonstrate the effectiveness of RippleRCA. Ablation studies and parameter sensitivity analysis further confirm the effectiveness of lag-aware causal priors, propagation response modeling, and the hierarchical two-stage design.