Causal explanations of outliers in systems with lagged time-dependencies

TL;DR

This paper extends causal root-cause analysis methods to systems with lagged dependencies, such as energy systems, by proposing truncation approaches to handle infinite causal graphs and benchmarking their effectiveness.

Contribution

It adapts a causal root-cause analysis method to general time-dependent systems and introduces truncation techniques to manage infinite dependency graphs.

Findings

The method can localize root-causes given sufficient lag information.

Different truncation approaches impact the accuracy of root-cause localization.

The approach is effective in a factory energy management scenario.

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.