Experimental Setup
Evaluation Metrics
- Precision@K (PR@K) \( PR@K = \frac{1}{|\mathbb{A}|}\sum_{a \in \mathbb{A}}\frac{\sum_{i < k}R_a(i)\in V_a}{\min (K, |v_a|)} \), where \(\mathbb{A}\) is the set of system faults, \(a\) is one fault in \(\mathbb{A}\), \(V_a\) is the real root causes of \(a\), \(R_a\) is the predicted root causes of \(a\), and \(i\) is the \(i\)-th predicted cause of \(R_a\)
- Mean Average Precision@K (MAP@K) \( MAP@K = \frac{1}{K|\mathbb{A}|} \sum_{a \in \mathbb{A}} \sum_{i\leq j\leq K} PR@j \)
- Mean Reciprocal Rank (MRR) \( MRR@K = \frac{1}{|\mathbb{A}|}\sum_{a \in \mathbb{A}}\frac{1}{rank_{R_a}}\), where \(rank_{R_a}\) is the rank number of the first correctly predicted root cause for system fault \(a\).
Baseline Methods
| Method | Main Technique | Single/Multi-modal | Time |
|---|---|---|---|
| PC-based | Constrain-based independence test + random walk | Single | 2020 |
| CIRCA | Bayesian network + intervention detection | Single | 2022 |
| ϵ-Diagnosis | Intervention-based causality + bounded-error diagnosis | Single | 2019 |
| RCD | Graph structure learning + statistical dependency analysis | Single | 2022 |
| BARO | Bayesian inference + probabilistic fault localization | Single | 2024 |
| Nezha | Graph neural networks + temporal dependency modeling | Multi-modal | 2023 |
For detailed experimental results, please refer to the experiment section in our paper.