The evolution of causal models: a comparison of bayesian metrics and structure priors

Julian R Neil, Kevin B Korb

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

    1 Citation (Scopus)


    We report the use genetic algorithms (GAs) as a search mechanism for the discovery of linear causal models when using two Bayesian metrics for linear causal models, a Minimum Message Length (MML) metric [10] and a full posterior analysis (BGe) [3]. We also consider two structure priors over causal models, one giving all variable orderings for models with the same arc density equal prior probability (P1) and one assigning all causal structures with the same arc density equal priors (P2). Evaluated with Kullback-Leibler distance prior P2 tended to produce models closer to the true model than P1 for both metrics, with MML performing slightly better than BGe. By contrast, when using an evaluation metric that better reflects the nature of the causal discovery task, namely a metric that compares the results of predictive performance on the effect nodes in the discovered model P1 outperformed P2 in general, with MML and BGe discovering models of similar predictive performance at various sample sizes. This supports our conjecture that the P1 prior is more appropriate for causal discovery.
    Original languageEnglish
    Title of host publicationMethodologies for Knowledge Discovery and Data Mining
    Subtitle of host publicationThird Pacific-Asia Conference, PAKDD-99 Beijing, China, April 26-28, 1999 Proceedings
    EditorsNing Zhong, Lizhu Zhou
    Place of PublicationBerlin Germany
    Number of pages6
    ISBN (Print)3540658661
    Publication statusPublished - 1999
    EventPacific-Asia Conference on Knowledge Discovery and Data Mining 1999 - Beijing, China
    Duration: 26 Apr 199928 Apr 1999
    Conference number: 3rd (Proceedings)

    Publication series

    NameLecture Notes in Artificial Intelligence
    ISSN (Print)0302-9743


    ConferencePacific-Asia Conference on Knowledge Discovery and Data Mining 1999
    Abbreviated titlePAKDD 1999
    Internet address

    Cite this