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.
View on arXiv@article{dawoud2025_2503.01475,
title={ ProRCA: A Causal Python Package for Actionable Root Cause Analysis in Real-world Business Scenarios },
author={ Ahmed Dawoud and Shravan Talupula },
journal={arXiv preprint arXiv:2503.01475},
year={ 2025 }
}