A characterization of Markov equivalence classes of Relational Causal Models under path semantics

Sanghack Lee, Vasant Honavar

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Scopus citations

Abstract

Relational Causal Models (RCM) generalize Causal Bayesian Networks so as to extend causal discovery to relational domains. We provide a novel and elegant characterization of the Markov equivalence of RCMs under path semantics. We introduce a novel representation of unshielded triples that allows us to efficiently determine whether an RCM is Markov equivalent to another. Under path semantics, we provide a sound and complete algorithm for recovering the structure of an RCM from conditional independence queries. Our analysis also suggests ways to improve the orientation recall of algorithms for learning the structure of RCM under bridge burning semantics as well.

Original languageEnglish (US)
Title of host publication32nd Conference on Uncertainty in Artificial Intelligence 2016, UAI 2016
EditorsDominik Janzing, Alexander Ihler
PublisherAssociation For Uncertainty in Artificial Intelligence (AUAI)
Pages387-396
Number of pages10
ISBN (Electronic)9781510827806
StatePublished - Jan 1 2016
Event32nd Conference on Uncertainty in Artificial Intelligence 2016, UAI 2016 - Jersey City, United States
Duration: Jun 25 2016Jun 29 2016

Publication series

Name32nd Conference on Uncertainty in Artificial Intelligence 2016, UAI 2016

Other

Other32nd Conference on Uncertainty in Artificial Intelligence 2016, UAI 2016
CountryUnited States
CityJersey City
Period6/25/166/29/16

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence

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  • Cite this

    Lee, S., & Honavar, V. (2016). A characterization of Markov equivalence classes of Relational Causal Models under path semantics. In D. Janzing, & A. Ihler (Eds.), 32nd Conference on Uncertainty in Artificial Intelligence 2016, UAI 2016 (pp. 387-396). (32nd Conference on Uncertainty in Artificial Intelligence 2016, UAI 2016). Association For Uncertainty in Artificial Intelligence (AUAI).