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 casual discovery works can be divided into two highly correlated categories according to whether the temporal data is calibrated, ie, multivariate time series casual discovery, and event sequence casual discovery. However, most previous surveys are only focused on the time series casual 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 casual discovery.

翻译：时间序列数据作为描述复杂系统随时间的观测结果，是一种典型的数据结构，广泛产生于工业、医疗和金融等领域。分析这类数据对各类应用极具价值。因此，过去数十年间，学者们提出了多种时间序列数据分析任务，例如分类、聚类和预测。其中，因果发现——从时间序列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。根据时间序列数据是否经过校准，现有因果发现工作可分为高度相关的两类：多元时间序列因果发现与事件序列因果发现。然而，此前绝大多数综述仅专注于时间序列因果发现，忽视了第二类。本文明确了两类任务之间的关联，并系统概述了现有解决方案。此外，我们还提供了公开数据集、评估指标以及时间序列数据因果发现的新视角。