Root Cause Analysis of Outliers in Unknown Cyclic Graphs
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arXiv
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866916997257756672 |
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| author | Schkoda, Daniela Janzing, Dominik |
| author_facet | Schkoda, Daniela Janzing, Dominik |
| contents | We study the propagation of outliers in cyclic causal graphs with linear structural equations, tracing them back to one or several "root cause" nodes. We show that it is possible to identify a short list of potential root causes provided that the perturbation is sufficiently strong and propagates according to the same structural equations as in the normal mode. This shortlist consists of the true root causes together with those of its parents lying on a cycle with the root cause. Notably, our method does not require prior knowledge of the causal graph. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_06995 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Root Cause Analysis of Outliers in Unknown Cyclic Graphs Schkoda, Daniela Janzing, Dominik Machine Learning Methodology We study the propagation of outliers in cyclic causal graphs with linear structural equations, tracing them back to one or several "root cause" nodes. We show that it is possible to identify a short list of potential root causes provided that the perturbation is sufficiently strong and propagates according to the same structural equations as in the normal mode. This shortlist consists of the true root causes together with those of its parents lying on a cycle with the root cause. Notably, our method does not require prior knowledge of the causal graph. |
| title | Root Cause Analysis of Outliers in Unknown Cyclic Graphs |
| topic | Machine Learning Methodology |
| url | https://arxiv.org/abs/2510.06995 |