Tracing Distribution Shifts with Causal System Maps

Joran Leest; Ilias Gerostathopoulos; Patricia Lago; Claudia Raibulet

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arXiv · 2510.23528

Tracing Distribution Shifts with Causal System Maps

Joran Leest·Ilias Gerostathopoulos·Patricia Lago·Claudia Raibulet

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Abstract

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.

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Joran Leest, Ilias Gerostathopoulos, Patricia Lago, Claudia Raibulet. 2025-10-27. Tracing Distribution Shifts with Causal System Maps. https://arxiv.org/abs/2510.23528

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