ResearchTrend.AI
  • Papers
  • Communities
  • Events
  • Blog
  • Pricing
Papers
Communities
Social Events
Terms and Conditions
Pricing
Parameter LabParameter LabTwitterGitHubLinkedInBlueskyYoutube

© 2025 ResearchTrend.AI, All rights reserved.

  1. Home
  2. Papers
  3. 2503.01475
32
0

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

3 March 2025
Ahmed Dawoud
Shravan Talupula
    CML
ArXivPDFHTML
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.

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 }
}
Comments on this paper