{"type":"ScholarlyArticle","title":"ASP-based Discovery of Semi-Markovian Causal Models under Weaker Assumptions","authors":[{"name":"Zhalama","slug":"zhalama","given":"","family":"Zhalama","affiliation":""},{"name":"Jiji Zhang","slug":"jiji-zhang","given":"Jiji","family":"Zhang","affiliation":"Lingnan University"},{"name":"Frederick Eberhardt","slug":"frederick-eberhardt","given":"Frederick","family":"Eberhardt","affiliation":"California Institute of Technology"},{"name":"Wolfgang Mayer","slug":"wolfgang-mayer","given":"Wolfgang","family":"Mayer","affiliation":"University of South Australia"},{"name":"Mark Junjie Li","slug":"mark-junjie-li","given":"Mark Junjie","family":"Li","affiliation":"Shenzhen University"}],"abstract":"In recent years the possibility of relaxing the so-called Faithfulness assumption in automated causal discovery has been investigated. The investigation showed (1) that the Faithfulness assumption can be weakened in various ways that in an important sense preserve its power, and (2) that weakening of Faithfulness may help to speed up methods based on Answer Set Programming. However, this line of work has so far only considered the discovery of causal models without latent variables. In this paper, we study weakenings of Faithfulness for constraint-based discovery of semi-Markovian causal models, which accommodate the possibility of latent variables, and show that both (1) and (2) remain the case in this more realistic setting.","doi":"10.24963/ijcai.2019/206","url":"https://ijcai.org/proceedings/2019/206","pdf":"https://ijcai.org/proceedings/2019/0206.pdf","markdown":"https://ijcai.org/proceedings/2019/206.md","bibtex":"https://ijcai.org/proceedings/2019/bibtex/206","volume":{"slug":"2019","title":"Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2019","url":"https://ijcai.org/proceedings/2019/","year":2019,"location":"Macao","dates":"10-16 August 2019","editors":"Sarit Kraus","isbn":"978-0-9992411-4-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2019"},"section":"Knowledge Representation and Reasoning","track":"Main track","number":206,"pages":{"first":1488,"last":1494},"keywords":["Machine Learning: Learning Graphical Models","Uncertainty in AI: Graphical Models","Knowledge Representation and Reasoning: Action, Change and Causality","Constraints and SAT: SAT: Applications"],"videos":[],"references":[{"position":1,"raw":"[Borboudakis and Tsamardinos, 2016] Giorgos Bor- boudakis and Ioannis Tsamardinos. 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