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Causal effect identification in acyclic directed mixed graphs and gated models
Linköping University, Department of Computer and Information Science, The Division of Statistics and Machine Learning. Linköping University, Faculty of Science & Engineering.
Linköping University, Department of Computer and Information Science, Database and information techniques. Linköping University, Faculty of Science & Engineering.ORCID iD: 0000-0002-8678-1164
2017 (English)In: International Journal of Approximate Reasoning, ISSN 0888-613X, E-ISSN 1873-4731, Vol. 90, 56-75 p.Article in journal (Refereed) Published
Abstract [en]

We introduce a new family of graphical models that consists of graphs with possibly directed, undirected and bidirected edges but without directed cycles. We show that these models are suitable for representing causal models with additive error terms. We provide a set of sufficient graphical criteria for the identification of arbitrary causal effects when the new models contain directed and undirected edges but no bidirected edge. We also provide a necessary and sufficient graphical criterion for the identification of the causal effect of a single variable on the rest of the variables. Moreover, we develop an exact algorithm for learning the new models from observational and interventional data via answer set programming. Finally, we introduce gated models for causal effect identification, a new family of graphical models that exploits context specific independences to identify additional causal effects. (C) 2017 Elsevier Inc. All rights reserved.

Place, publisher, year, edition, pages
Elsevier, 2017. Vol. 90, 56-75 p.
Keyword [en]
Acyclic directed mixed graphs; Causal models; Answer set programming
National Category
Computer Systems
Identifiers
URN: urn:nbn:se:liu:diva-142975DOI: 10.1016/j.ijar.2017.06.015ISI: 000413380900004Scopus ID: 2-s2.0-85024493987OAI: oai:DiVA.org:liu-142975DiVA: diva2:1156559
Available from: 2017-11-13 Created: 2017-11-13 Last updated: 2017-11-29Bibliographically approved

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Pena, Jose MBendtsen, Marcus
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