Distributed cloud systems exhibit complex interdependencies across microservices, infrastructure, and network layers, making root cause analysis (RCA) challenging during failures. Traditional observability approaches rely on correlation-based techniques that often produce ambiguous or misleading diagnostic signals, contributing to elevated mean time to resolution (MTTR) and increased operational risk. This paper proposes CausalRCA, a causal graph-based framework for root cause analysis in distributed cloud environments. By constructing directed acyclic graphs (DAGs) from multi-modal telemetry data metrics, logs, and distributed traces and applying structural causal models (SCMs) with do-calculus interventions, the framework identifies true root causes rather than correlated symptoms. The framework integrates with standard cloud-native observability stacks including Prometheus, Jaeger, and OpenTelemetry, and was evaluated on a representative Kubernetes-based microservices testbed with controlled fault injection. Experimental results demonstrate a 35% improvement in root cause identification accuracy, a 28 % reduction in MTTR, and a substantially lower false positive rate in cascading failure scenarios compared to leading correlation-based RCA methods. These results establish causal inference as a practical and deployable paradigm for production cloud operations.
Kodali et al. (2024) studied this question.