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.

翻译：受控时间依赖系统中的根本原因分析在应用中构成重大挑战。能源系统尤其难以处理，因为它们同时表现出瞬时效应与延迟效应，若配备储能装置则具备记忆特性。本文基于Budhathoki等人[2022]提出的因果根本原因分析方法，将其推广至一般时间依赖系统，该方法可视为"根本原因"概念的严格因果定义。特别地，我们讨论了两种截断方法来处理时间依赖系统中存在的无限依赖图：一种保持因果机制完整，另一种则在起始节点处对机制进行近似。通过受工厂能源管理问题启发的具有挑战性的数据生成过程——即电力消耗峰值规避问题——对不同方法的有效性进行基准测试。研究表明，在给定足够滞后阶数的情况下，我们的扩展方法能够定位特征域与时间域中的根本原因。此外，本文还探讨了机制近似处理的影响效应。