ProRCA: A Causal Python Package for Actionable Root Cause Analysis in Real-world Business Scenarios

• URL: http://arxiv.org/abs/2503.01475v1
• Date: Mon, 03 Mar 2025 12:33:17 GMT
• Title: ProRCA: A Causal Python Package for Actionable Root Cause Analysis in Real-world Business Scenarios
• Authors: Ahmed Dawoud, Shravan Talupula,
• Abstract summary: We present a pathway-tracing package built on the DoWhy causal inference library.<n>Our method integrates conditional anomaly scoring, noise-based attribution, and depth-first path exploration to reveal multi-hop causal chains.
• Score: 2.034531141644187
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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