Causal Discovery in Non-Ideal Frameworks

Stijn Meganck, Philippe Leray, Bernard Manderick, Mohand Boughanem (Editor), Salem Benferhat (Editor), Guy Melançon (Editor)

    Research output: Contribution to journalArticlepeer-review


    A Causal Bayesian network (CBN) is a well-known technique to represent causal
    relations between variables. To learn the causal relations, experiments are needed in
    general because from observational data alone we can only learn up to Markov
    equivalence. The elicitation of this causal information from data provides useful insight in
    the system under study. Most existing algorithms for causal discovery implicitly assume a
    setting where all variables of the system under study are known and sufficient amount of
    data is present to perform reliable statistical tests. These assumptions provide an ideal
    framework, which facilitates the discovery task significantly. In this article we use existing
    techniques from this ideal setting as a starting point for new algorithms in non-ideal
    settings. We present two algorithms, UnCaDo and MyCaDo++, which can be used when
    insufficient data is present to perform statistical tests reliably and when there are latent
    variables respectively. Both techniques provide experimentation plans to recover the
    causal structure.
    Original languageEnglish
    Number of pages35
    JournalI3 Journal
    Publication statusPublished - 2009

    Bibliographical note

    Mohand Boughanem, Salem Benferhat, Guy Melançon


    • Causality
    • Bayesian Networks
    • Probabilistic Graphical Models


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