  Time series data is a collection of chronological observations which is
generated by several domains such as medical and financial fields. Over the
years, different tasks such as classification, forecasting, and clustering have
been proposed to analyze this type of data. Time series data has been also used
to study the effect of interventions over time. Moreover, in many fields of
science, learning the causal structure of dynamic systems and time series data
is considered an interesting task which plays an important role in scientific
discoveries. Estimating the effect of an intervention and identifying the
causal relations from the data can be performed via causal inference. Existing
surveys on time series discuss traditional tasks such as classification and
forecasting or explain the details of the approaches proposed to solve a
specific task. In this paper, we focus on two causal inference tasks, i.e.,
treatment effect estimation and causal discovery for time series data, and
provide a comprehensive review of the approaches in each task. Furthermore, we
curate a list of commonly used evaluation metrics and datasets for each task
and provide in-depth insight. These metrics and datasets can serve as
benchmarks for research in the field.
