Constructing separators and adjustment sets in ancestral graphs

Benito Van Der Zander, Maciej Liśkiewicz, Johannes Textor

    Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review


    Ancestral graphs (AGs) are graphical causal models that can represent uncertainty about the presence of latent confounders, and can be inferred from data. Here, we present an algorithmic framework for efficiently testing, constructing, and enumerating m-separators in AGs. Moreover, we present a new constructive criterion for covariate adjustment in directed acyclic graphs (DAGs) and maximal ancestral graphs (MAGs) that characterizes adjustment sets as mseparators in a subgraph. Jointly, these results allow to find all adjustment sets that can identify a desired causal effect with multivariate exposures and outcomes in the presence of latent confounding. Our results generalize and improve upon several existing solutions for special cases of these problems.

    Original languageEnglish
    Title of host publicationUncertainty in Artificial Intelligence - Proceedings of the 30th Conference, UAI 2014
    PublisherAUAI Press
    Number of pages10
    ISBN (Print)9780974903910
    Publication statusPublished - 2014
    Event30th Conference on Uncertainty in Artificial Intelligence, UAI 2014 - Quebec City, Canada
    Duration: 23 Jul 201427 Jul 2014


    Conference30th Conference on Uncertainty in Artificial Intelligence, UAI 2014
    CityQuebec City


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