NetCause: Counterfactual Learning for Root Cause Analysis in Large-Scale Networks
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 natu...
| Authors | Fabien Chraim, Jian Zhang, Dominik Janzing et al. |
|---|---|
| Primary category | cs.NI |
| Published | 11 June 2026 |
| Last updated at source | 11 June 2026 |
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