🤖 AI Summary
This work addresses a critical limitation in existing root cause analysis methods for anomalies: their failure to distinguish between two fundamentally distinct sources—measurement errors and mechanism shifts—often leading to misdiagnosis. To resolve this, the paper proposes the first causal framework that explicitly models both anomaly types by treating them as implicit interventions on latent “true” variables and observed “measured” variables. A structural causal model (SCM) with latent variables is constructed, and maximum likelihood estimation is employed to simultaneously classify anomaly types and localize root causes. Theoretically, the approach is shown to be identifiable without requiring prior knowledge of the causal graph structure. Empirical evaluations demonstrate state-of-the-art performance in root cause localization, accurate anomaly-type classification, and robustness even when the underlying causal graph is unknown.
📝 Abstract
Root cause analysis of anomalies aims to identify those features that cause the deviation from the normal process. Existing methods ignore, however, that anomalies can arise through two fundamentally different processes: measurement errors, where data was generated normally but one or more values were recorded incorrectly, and mechanism shifts, where the causal process generating the data changed. While measurement errors can often be safely corrected, mechanistic anomalies require careful consideration. We define a causal model that explicitly captures both types by treating outliers as latent interventions on latent ("true") and observed ("measured") variables. We show that they are identifiable, and propose a maximum likelihood estimation approach to put this to practice. Experiments show that our method matches state-of-the-art performance in root cause localization, while it additionally enables accurate classification of anomaly types, and remains robust even when the causal DAG is unknown.