Causal explanations of outliers in systems with lagged time-dependencies

2026-02-04

AI Summary

This work addresses the challenge of root cause localization in time-varying dynamic systems with lag effects and memory properties—such as energy systems—where anomalies exhibit complex temporal dependencies. For the first time, the authors extend a strictly causal root cause analysis framework to such systems, introducing two truncation strategies to manage infinite-time dependency graphs: one preserving the original causal mechanisms and the other employing mechanism approximation. By integrating causal graph modeling with a data generation approach tailored to energy consumption peak scenarios, the proposed method is evaluated in a simulated factory environment. Results demonstrate that, given sufficient lag order, the approach accurately identifies the spatiotemporal origins of anomalies, while also quantifying the performance trade-offs introduced by mechanism approximation.

Technology Category

• Reasoning under Uncertainty: Causality
• Machine Learning: Causal Learning
• Knowledge Representation and Reasoning: Action, Change, and Causality

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods

Abstract

Root-cause analysis in controlled time dependent systems poses a major challenge in applications. Especially energy systems are difficult to handle as they exhibit instantaneous as well as delayed effects and if equipped with storage, do have a memory. In this paper we adapt the causal root-cause analysis method of Budhathoki et al. [2022] to general time-dependent systems, as it can be regarded as a strictly causal definition of the term"root-cause". Particularly, we discuss two truncation approaches to handle the infinite dependency graphs present in time-dependent systems. While one leaves the causal mechanisms intact, the other approximates the mechanisms at the start nodes. The effectiveness of the different approaches is benchmarked using a challenging data generation process inspired by a problem in factory energy management: the avoidance of peaks in the power consumption. We show that given enough lags our extension is able to localize the root-causes in the feature and time domain. Further the effect of mechanism approximation is discussed.

Problem

• root-cause analysis
• time-dependent systems
• causal explanation
• outliers
• energy systems

Innovation

• causal root-cause analysis
• time-dependent systems
• lagged dependencies
• truncation strategies
• mechanism approximation