  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.
