Causal Inference in Distributed Tracing: Automating Root Cause Analysis in Complex Microservice Dependencies

Abstract

The architectural shift to microservice ecosystems has created a root cause analysis (RCA) crisis in production operations, where manual investigation of distributed system failures can consume 47 minutes or more of critical incident response time. This paper presents TraceCausalNet, a causal inference framework for automated RCA in complex microservice dependencies, implemented and validated across hundreds of production incidents in a regulated financial services environment. The proposed framework constructs dynamic service dependency graphs from distributed trace data collected via Dynatrace, with anomalies detected by Dynatrace Davis AI triggering PagerDuty on-call engagement — and causal analysis beginning immediately upon problem detection, before the on-call engineer begins manual investigation. Granger causality analysis identifies causal propagation paths across interdependent services and ranks root cause candidates by interventional impact score. Evaluated against a production baseline of 47 minutes mean time to root cause (MTTRC), the framework achieved an 8-minute average diagnosis time representing an 83% reduction with 91% top-3 root cause accuracy spanning six credit union banking applications over four years. These results demonstrate that causal inference-based RCA, when grounded in production trace data rather than synthetic benchmarks, substantially outperforms both manual investigation and correlation-based automated approaches. The framework architecture, evaluation methodology, and deployment considerations in SOC 2 regulated environments are presented as a reproducible contribution to the AIOps and SRE engineering communities.