Towards characterizing Markov equivalence classes for directed acyclic graphs with latent variables

Ayesha ALI, Thomas RICHARDSON, Peter SPIRTES, Jiji ZHANG

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    20 Citations (Scopus)

    Abstract

    It is well known that there may be many causal explanations that are consistent with a given set of data. Recent work has been done to represent the common aspects of these explanations into one representation. In this paper, we address what is less well known: how do the relationships common to every causal explanation among the observed variables of some DAG process change in the presence of latent variables? Ancestral graphs provide a class of graphs that can encode conditional independence relations that arise in DAG models with latent and selection variables. In this paper we present a set of orientation rules that construct the Markov equivalence class representative for ancestral graphs, given a member of the equivalence class. These rules are sound and complete. We also show that when the equivalence class includes a DAG, the equivalence class representative is the essential graph for the said DAG
    Original languageEnglish
    Title of host publicationProceedings of the Twenty-First Conference Conference on Uncertainty in Artificial Intelligence (2005)
    PublisherAUAI Press
    Pages10-17
    Number of pages8
    ISBN (Print)974903914
    Publication statusPublished - 1 Jan 2005

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