Survey and Evaluation of Causal Discovery Methods for Time Series

We introduce in this survey the major concepts, models, and algorithms proposed so far to infer causal relations from observational time series, a task usually referred as discovery series. To do so, after description of underlying concepts modelling assumptions, we present different methods according family approaches they belong to: Granger causality, constraint-based approaches, noise-based score-based logic-based topology-based difference-based approaches. then evaluate several representative illustrate behaviour families This illustration is conducted on both artificial real datasets, with characteristics. The main conclusions one can draw that times series an active research field which new (in every approaches) are regularly proposed, no or method stands out all situations. Indeed, rely assumptions may not be appropriate for particular dataset.