ASP-based discovery of semi-Markovian causal models under weaker assumptions

Zhalama, Jiji ZHANG, Frederick EBERHARDT, Wolfgang MAYER, Mark Junjue LI

Research output: Book Chapters | Papers in Conference ProceedingsConference paper (refereed)

Abstract

In recent years the possibility of relaxing the socalled 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.
Original languageEnglish
Title of host publicationProceedings of the 28th International Joint Conference on Artificial Intelligence, IJCAI 2019
EditorsSarit Kraus
PublisherInternational Joint Conferences on Artificial Intelligence
Pages1488-1494
Number of pages7
ISBN (Electronic)9780999241141
Publication statusPublished - 10 Aug 2019
Event28th International Joint Conference on Artificial Intelligence, IJCAI 2019 - Macao, China
Duration: 10 Aug 201916 Aug 2019
https://www.ijcai19.org/

Publication series

NameIJCAI International Joint Conference on Artificial Intelligence
Volume2019-August
ISSN (Print)1045-0823

Conference

Conference28th International Joint Conference on Artificial Intelligence, IJCAI 2019
Abbreviated titleIJCAI2019
CountryChina
CityMacao
Period10/08/1916/08/19
Internet address

Cite this

Zhalama, ZHANG, J., EBERHARDT, F., MAYER, W., & LI, M. J. (2019). ASP-based discovery of semi-Markovian causal models under weaker assumptions. In S. Kraus (Ed.), Proceedings of the 28th International Joint Conference on Artificial Intelligence, IJCAI 2019 (pp. 1488-1494). (IJCAI International Joint Conference on Artificial Intelligence; Vol. 2019-August). International Joint Conferences on Artificial Intelligence. https://www.ijcai.org/proceedings/2019/0206.pdf