NetCause: Counterfactual Learning for Root Cause Analysis in Large-Scale Networks

• URL: http://arxiv.org/abs/2606.13543v1
• Date: Thu, 11 Jun 2026 16:26:33 GMT
• Title: NetCause: Counterfactual Learning for Root Cause Analysis in Large-Scale Networks
• Abstract summary: We present NetCause, a self-supervised learning-based framework that models network incidents as graph-temporal processes.<n>We train the model on over 1,500 incidents collected over six months from a leading cloud provider's production network and evaluate it on 31 expert-labeled incidents.
• Score: 19.973156931073447
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Can a learned model capture how faults propagate through a large-scale network and use this knowledge to causally attribute customer impact to its underlying root cause? Existing root cause analysis techniques often rely on static rules, correlation heuristics, or topology-local reasoning, which struggle to generalize in dynamic environments where faults propagate across complex physical and logical dependencies. We present NetCause, a self-supervised learning-based framework that models network incidents as graph-temporal processes and uses counterfactual simulation to rank candidate root causes. This approach produces an interpretable ranking of root cause hypotheses and integrates naturally with operator-defined mitigation and remediation actions. We train the model on over 1,500 incidents collected over six months from a leading cloud provider's production network and evaluate it on 31 expert-labeled incidents. NetCause consistently improves root cause ranking quality in the regime most relevant to operational decision-making, achieving a 16.1% accuracy improvement over a rule-based heuristic baseline. While training is computationally intensive, inference is lightweight, requiring only seconds of GPU runtime per incident (well below typical telemetry collection latencies).

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