Temporal data, representing chronological observations of complex systems, has always been a typical data structure that can be widely generated by many domains, such as industry, medicine and finance. Analyzing this type of data is extremely valuable for various applications. Thus, different temporal data analysis tasks, eg, classification, clustering and prediction, have been proposed in the past decades. Among them, causal discovery, learning the causal relations from temporal data, is considered an interesting yet critical task and has attracted much research attention. Existing causal discovery works can be divided into two highly correlated categories according to whether the temporal data is calibrated, ie, multivariate time series causal discovery, and event sequence causal discovery. However, most previous surveys are only focused on the time series causal discovery and ignore the second category. In this paper, we specify the correlation between the two categories and provide a systematical overview of existing solutions. Furthermore, we provide public datasets, evaluation metrics and new perspectives for temporal data causal discovery.

翻译：时序数据作为对复杂系统按时间顺序观测的结果，一直是工业、医疗、金融等领域广泛产生的一种典型数据结构。分析这类数据对各类应用具有极高价值。因此，过去数十年间，学界提出了不同的时序数据分析任务，例如分类、聚类与预测。其中，因果发现——即从时序数据中学习因果关系——被认为是一项有趣且关键的任务，并吸引了大量研究关注。根据时序数据是否经过校准，现有因果发现工作可分为两个高度相关的类别：多元时间序列因果发现与事件序列因果发现。然而，以往的大多数综述仅聚焦于时间序列因果发现，而忽略了第二类别。本文明确了这两个类别之间的关联，并对现有方法进行了系统性概述。此外，我们还提供了公开数据集、评估指标以及面向时序数据因果发现的新视角。