{"type":"ScholarlyArticle","title":"Survey and Evaluation of Causal Discovery Methods for Time Series (Extended Abstract)","authors":[{"name":"Charles K. Assaad","slug":"charles-k-assaad","given":"Charles K.","family":"Assaad","affiliation":"Univ. Grenoble Alpes , CNRS , Grenoble INP , LIG; EasyVista"},{"name":"Emilie Devijver","slug":"emilie-devijver","given":"Emilie","family":"Devijver","affiliation":"Univ. Grenoble Alpes , CNRS , Grenoble INP , LIG"},{"name":"Eric Gaussier","slug":"eric-gaussier","given":"Eric","family":"Gaussier","affiliation":"Univ. Grenoble Alpes , CNRS , Grenoble INP , LIG"}],"abstract":"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 to as causal discovery in time series. To do so, after a description of the underlying concepts and modelling assumptions, we present different methods according to the family of approaches they belong to: Granger causality, constraint-based approaches, noise-based approaches, score-based approaches, logic-based approaches, topology-based approaches, and difference-based approaches. We then evaluate several representative methods to illustrate the behaviour of different families of approaches. This illustration is conducted on both artificial and real datasets, with different characteristics. The main conclusions one can draw from this survey is that causal discovery in times series is an active research field in which new methods (in every family of approaches) are regularly proposed, and that no family or method stands out in all situations. Indeed, they all rely on assumptions that may or may not be appropriate for a particular dataset.","doi":"10.24963/ijcai.2023/766","url":"https://ijcai.org/proceedings/2023/766","pdf":"https://ijcai.org/proceedings/2023/0766.pdf","markdown":"https://ijcai.org/proceedings/2023/766.md","bibtex":"https://ijcai.org/proceedings/2023/bibtex/766","volume":{"slug":"2023","title":"Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2023","url":"https://ijcai.org/proceedings/2023/","year":2023,"location":"Macao, SAR","dates":"19-25 August 2023","editors":"Edith Elkind","isbn":"978-1-956792-03-4","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2023"},"section":"Journal Track","track":"Journal Track","number":766,"pages":{"first":6839,"last":6844},"keywords":["Uncertainty in AI: UAI: Causality, structural causal models and causal inference","Machine Learning: ML: Time series and data streams"],"videos":[],"references":[{"position":1,"raw":"[Assaad et al., 2021] Charles K. 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