ProRCA: A Causal Python Package for Actionable Root Cause Analysis in Real-world Business Scenarios
This provides an actionable RCA solution for businesses dealing with dynamic, multi-layered environments, but it is incremental as it builds on existing causal inference libraries.
The paper tackles the problem of root cause analysis in complex systems by introducing a causal inference package that traces multi-hop causal chains from anomalies to triggers, demonstrating accurate trigger isolation and root cause ranking in synthetic experiments.
Root Cause Analysis (RCA) is becoming ever more critical as modern systems grow in complexity, volume of data, and interdependencies. While traditional RCA methods frequently rely on correlation-based or rule-based techniques, these approaches can prove inadequate in highly dynamic, multi-layered environments. In this paper, we present a pathway-tracing package built on the DoWhy causal inference library. Our method integrates conditional anomaly scoring, noise-based attribution, and depth-first path exploration to reveal multi-hop causal chains. By systematically tracing entire causal pathways from an observed anomaly back to the initial triggers, our approach provides a comprehensive, end-to-end RCA solution. Experimental evaluations with synthetic anomaly injections demonstrate the package's ability to accurately isolate triggers and rank root causes by their overall significance.