Tracing Distribution Shifts with Causal System Maps
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866918173337452544 |
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| author | Leest, Joran Gerostathopoulos, Ilias Lago, Patricia Raibulet, Claudia |
| author_facet | Leest, Joran Gerostathopoulos, Ilias Lago, Patricia Raibulet, Claudia |
| contents | Monitoring machine learning (ML) systems is hard, with standard practice focusing on detecting distribution shifts rather than their causes. Root-cause analysis often relies on manual tracing to determine whether a shift is caused by software faults, data-quality issues, or natural change. We propose ML System Maps -- causal maps that, through layered views, make explicit the propagation paths between the environment and the ML system's internals, enabling systematic attribution of distribution shifts. We outline the approach and a research agenda for its development and evaluation. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_23528 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Tracing Distribution Shifts with Causal System Maps Leest, Joran Gerostathopoulos, Ilias Lago, Patricia Raibulet, Claudia Software Engineering Monitoring machine learning (ML) systems is hard, with standard practice focusing on detecting distribution shifts rather than their causes. Root-cause analysis often relies on manual tracing to determine whether a shift is caused by software faults, data-quality issues, or natural change. We propose ML System Maps -- causal maps that, through layered views, make explicit the propagation paths between the environment and the ML system's internals, enabling systematic attribution of distribution shifts. We outline the approach and a research agenda for its development and evaluation. |
| title | Tracing Distribution Shifts with Causal System Maps |
| topic | Software Engineering |
| url | https://arxiv.org/abs/2510.23528 |