Leveraging Causal Inference Techniques for Robust Root Cause Identification in Complex Systems

Root cause identification is a critical task in various domains, from industrial processes to healthcare diagnostics.Traditional methods often struggle with the complexity and interdependencies present in modern systems.This paper presents a comprehensive framework for leveraging causal inference techniques to enhance root cause identification in complex systems.By integrating structural causal models, counterfactual analysis, and interventional methods, we propose a robust approach to uncover causal relationships and identify true root causes.Our methodology encompasses data preprocessing, causal discovery, hypothesis testing, and validation.The proposed framework aims to distinguish between mere correlations and actual causal relationships, leading to more accurate and actionable insights.This research contributes to the field of causal inference and its practical applications, providing practitioners with advanced tools for tackling root cause identification challenges in diverse scenarios.