Causal Discovery from Temporal Data: An Overview and New Perspectives

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, finance, healthcare, and climatology. Analyzing the underlying structures, i.e., the causal relations, could be extremely valuable for various applications. Recently, causal discovery from temporal data has been considered as an interesting yet critical task and attracted much research attention. According to the nature and structure of temporal data, existing causal discovery works can be divided into two highly correlated categories i.e., multivariate time series causal discovery, and event sequence causal discovery. However, most previous surveys are only focused on the multivariate time series causal discovery but ignore the second category. In this article, we specify the similarity between the two categories and provide an overview of existing solutions. Furthermore, we provide public datasets, evaluation metrics, and new perspectives for temporal data causal discovery.