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

Problem
Existing root cause analysis (RCA) techniques often rely on static rules, correlation heuristics, or topology-local reasoning, which are inadequate in dynamic environments where faults propagate through complex physical and logical dependencies. This paper addresses the gap in the literature regarding the need for a more robust, generalizable approach to RCA in large-scale networks. The authors propose a novel framework, NetCause, which leverages self-supervised learning to improve the identification of root causes in network incidents. This work is presented as a preprint and has not yet undergone peer review.

Method
NetCause models network incidents as graph-temporal processes, allowing it to capture the dynamic nature of fault propagation. The framework employs counterfactual simulation to evaluate and rank potential root causes based on their impact on customer experience. The model is trained on a dataset comprising over 1,500 incidents collected over six months from a leading cloud provider’s production network. The training process is computationally intensive, although the inference phase is efficient, requiring only seconds of GPU runtime per incident. The authors do not disclose specific architectural details or the exact loss function used, but emphasize the self-supervised learning paradigm that enables the model to learn from the data without extensive labeling.

Results
NetCause demonstrates a significant improvement in root cause ranking quality, achieving a 16.1% accuracy increase over a rule-based heuristic baseline. The evaluation is conducted on 31 expert-labeled incidents, indicating that the model is particularly effective in scenarios relevant to operational decision-making. The results suggest that NetCause can provide more accurate and interpretable rankings of root cause hypotheses compared to traditional methods.

Limitations
The authors acknowledge that the training of NetCause is computationally intensive, which may limit its applicability in environments with constrained resources. Additionally, while the model shows promise in improving RCA, it is still reliant on the quality of the input data and the underlying assumptions of the graph-temporal modeling approach. The paper does not address potential biases in the training data or the generalizability of the model to different network topologies or incident types.

Why it matters
The implications of this work are significant for operational efficiency in large-scale networks, as accurate RCA can lead to faster mitigation and remediation actions, ultimately enhancing service reliability and customer satisfaction. The integration of operator-defined actions with the model’s output allows for a more tailored response to incidents. This research contributes to the growing body of literature on self-supervised learning applications in network management and fault analysis, as published in arXiv cs.LG.