Root Cause Analysis with Latent Confounders using Partial Ancestral Graphs

Researchers introduce PAG-RCA, a framework for root cause analysis in complex systems that accounts for unobserved latent variables using Partial Ancestral Graphs. The methodology combines causal identification with partial identification bounds to diagnose system failures reliably even when data is scarce or incomplete, outperforming existing approaches on synthetic and real-world infrastructure benchmarks.

PAG-RCA addresses a fundamental limitation in automated failure diagnosis: most existing root cause analysis systems assume all relevant variables are observable, an assumption rarely valid in production environments. This research integrates latent variable handling with causal inference, enabling systems to identify failure sources when confounding factors remain unmeasured. The framework's dual approach—using standard causal identification when possible and analytical bounds when effects are structurally unidentifiable—provides practitioners with confidence intervals for diagnosis rather than false certainty.

The advancement matters because modern infrastructure increasingly relies on automated anomaly detection and self-healing systems. Microservices, distributed networks, and critical infrastructure like power grids generate complex failure modes where hidden variables (network latency, hardware degradation, external dependencies) significantly impact diagnosis. Traditional methods degrade substantially under these conditions, leading to misdiagnosed failures and extended downtime.

For organizations operating complex systems, PAG-RCA improves diagnostic accuracy without requiring complete observability—a practical advantage since instrumenting all system interactions remains economically infeasible. The framework's demonstrated performance on microservice and power-grid benchmarks suggests immediate applicability to cloud infrastructure, financial trading systems, and energy networks.

Future development should focus on computational scalability for large-scale systems and real-time diagnostic deployment. The integration of partial identification into root cause analysis opens possibilities for extending the methodology to dynamic systems and online learning scenarios where latent confounders evolve over time.

• →PAG-RCA enables root cause analysis in systems with unobserved latent variables, addressing real-world observability constraints.
• →The framework combines causal identification with partial identification bounds for robust diagnosis under structural unidentifiability.
• →Performance testing on microservice and power-grid data demonstrates consistent improvements over existing data-driven baselines.
• →The methodology reduces diagnostic degradation in data-scarce scenarios where traditional causal approaches fail.
• →Integration of latent variable handling enables reliable automated diagnostics in partially observable complex networks.