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

2025-03-03

AI Summary

Root cause localization in complex, dynamic multi-layer business systems suffers from poor interpretability and difficulty in tracing multi-hop causal dependencies. Method: This paper proposes an end-to-end causal inference framework that uniquely integrates conditional anomaly scoring, counterfactual noise attribution, and depth-first graph search—implemented atop DoWhy—to enable interpretable, backward tracing of multi-hop causal paths from observed anomalies to their initial triggers. Unlike conventional correlation- or rule-based approaches, it reconstructs the full causal chain rather than identifying isolated correlations. Contribution/Results: Evaluated on synthetic anomaly injection benchmarks, the framework achieves significantly higher root cause ranking accuracy than state-of-the-art baselines. It supports actionable root cause diagnosis in dynamic environments by delivering both precise causal attribution and human-interpretable explanations grounded in structural causal models.

Technology Category

• Reasoning under Uncertainty: Causality
• Machine Learning: Causal Learning
• Knowledge Representation and Reasoning: Action, Change, and Causality

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems

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.

Problem

• Addresses limitations of traditional RCA methods in complex systems
• Develops a causal inference package for actionable root cause analysis
• Identifies and ranks root causes by tracing multi-hop causal chains

Innovation

• Uses DoWhy library for causal inference
• Integrates anomaly scoring and path exploration
• Traces multi-hop causal chains systematically