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arXiv Machine Learning By Hendrik Suhr, David Kaltenpoth, Jilles Vreeken

Root Cause Analysis of Measurement and Mechanistic Anomalies

Read the original on arXiv Machine Learning →

The paper introduces a causal framework that distinguishes between measurement errors and mechanism shifts as two distinct sources of anomalies. By modeling outliers as latent interventions on both true and observed variables, the authors derive conditions under which these anomaly types can be differentiated. They present an efficient inference algorithm that localizes root causes and classifies anomaly types, achieving state‑of‑the‑art performance on synthetic and real‑world datasets.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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