Causal explanations of outliers in systems with lagged time-dependencies
This work addresses root-cause analysis for energy systems with memory effects, but it is incremental as it adapts an existing method to a more general time-dependent context.
The paper tackles root-cause analysis in time-dependent systems with lagged dependencies by adapting an existing causal method to handle infinite dependency graphs, demonstrating its ability to localize root-causes in feature and time domains for energy management problems.
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.