🤖 AI Summary
This work addresses the limitations of existing data-driven root cause analysis methods, which rely on the causal sufficiency assumption and suffer significant performance degradation in partially observable systems with unmeasured latent variables. The authors propose a novel approach based on partial ancestral graphs (PAGs), modeling system failures as parametric interventions and integrating causal effect identification with partial identification theory to rank candidate root causes. Notably, for non-identifiable scenarios, the method introduces analytical causal bounds for the first time in root cause analysis. This framework is the first to jointly handle latent variables and partial identifiability, thereby eliminating dependence on causal sufficiency. Experiments demonstrate that the proposed method substantially outperforms state-of-the-art approaches across synthetic data, microservice anomaly benchmarks, and power grid cascading failure datasets, confirming its robustness and effectiveness in complex, partially observable environments.
📝 Abstract
Finding the source of failures, known as Root Cause Analysis (RCA), is essential for identifying the root causes of anomalies and maintaining the reliability of complex systems. While causal theory has advanced data-driven RCA, existing frameworks assume causal sufficiency, failing to account for the unobserved latent variables prevalent in real-world environments. To address this gap, we propose PAG-RCA. This framework models system failures as parametric interventions over Partial Ancestral Graphs (PAGs) to perform RCA in the presence of latent variables. We use standard causal identification algorithms to find the source of failures by quantifying causal effects over the PAG. When an effect is identifiable, candidate root causes are ranked based on their exact intervention effects. When effects are structurally unidentifiable, our framework (for the first time in the RCA literature) integrates partial identification to evaluate and score candidates using analytical causal bounds. By integrating latent variables and partial identification at once our framework ensures robust RCA even under data scarcity and latent-variable scenarios where traditional methods degrade. Evaluations on synthetic data, microservice anomaly benchmarks and power-grid cascading failures demonstrate that PAG-RCA consistently outperforms state-of-the-art data-driven baselines. By improving data-driven RCA performance under data scarcity, this methodology advances reliable automated diagnostics in partially observable complex networks.