On causal identification under Markov equivalence

Amin JABER, Jiji ZHANG, Elias BAREINBOIM

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

Abstract

In this work, we investigate the problem of computing an experimental distribution from a combination of the observational distribution and a partial qualitative description of the causal structure of the domain under investigation. This description is given by a partial ancestral graph (PAG) that represents a Markov equivalence class of causal diagrams, i.e., diagrams that entail the same conditional independence model over observed variables, and is learnable from the observational data. Accordingly, we develop a complete algorithm to compute the causal effect of an arbitrary set of intervention variables on an arbitrary outcome set.

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
Pages6181-6185
Number of pages5
ISBN (Electronic)9780999241141
Publication statusPublished - 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

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Cite this

JABER, A., ZHANG, J., & BAREINBOIM, E. (2019). On causal identification under Markov equivalence. In S. Kraus (Ed.), Proceedings of the 28th International Joint Conference on Artificial Intelligence, IJCAI 2019 (pp. 6181-6185). (IJCAI International Joint Conference on Artificial Intelligence; Vol. 2019-August). International Joint Conferences on Artificial Intelligence.