@prefix dcat: <http://www.w3.org/ns/dcat#> .
@prefix dct: <http://purl.org/dc/terms/> .
@prefix foaf: <http://xmlns.com/foaf/0.1/> .
@prefix vcard: <http://www.w3.org/2006/vcard/ns#> .
@prefix xsd: <http://www.w3.org/2001/XMLSchema#> .

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    dct:description """
              From conventional observation data , it is rarely possible to determine a fully causal Bayesian network. The theoretical point at which we are interested is learning causal Bayesian networks , with or without latent variables. We first focused on the discovery of causal relationships when all variables are known ( ie there are no latent variables ) proposing a learning algorithm using both data from observations and experiments. Logically, we then focused on the same problem when all the variables are not known . We must therefore discover both causal relationships between variables and the presence of latent variables in a Bayesian network structure. To do this, we try to unify two formalisms , semi- Markovian causal models (SMCM) and maximum ancestral graphs (MAG), previously used separately , one for causal inference (SMCM), the other for the discovery of causality (MAG) . We are also interested in the adaptation of causal Bayesian networks for multi -agent systems, and learning these multi-agent causal models (MACM) .
            """ ;
    dct:identifier "tel-00915256" ;
    dct:issued "2026-05-07T22:08:48.269851"^^xsd:dateTime ;
    dct:language "en" ;
    dct:modified "2026-05-07T22:08:48.269855"^^xsd:dateTime ;
    dct:publisher <https://rec.harvest-normandie.data4citizen.com/organization/cce9db95-46d9-4dc2-84b6-764215d0a002> ;
    dct:title "Towards an Integral Approach for Modeling Causality" ;
    dcat:contactPoint [ a vcard:Organization ;
            vcard:fn "CCSD" ] ;
    dcat:distribution <https://rec.harvest-normandie.data4citizen.com/dataset/oai-hal-tel-00915256v1/resource/c817c730-e568-4ca0-afe6-2b654f76338f> ;
    dcat:keyword "bayesian-network",
        "causal-discovery",
        "infoeu-reposemanticsdoctoralthesis",
        "infoinfo-aicomputer-science-csartificial-intelligence-csai",
        "infoinfo-lgcomputer-science-csmachine-learning-cslg",
        "learning",
        "theses" ;
    dcat:landingPage <https://theses.hal.science/tel-00915256> .

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    dct:format "HTML" ;
    dct:issued "2026-05-07T22:08:48.272911"^^xsd:dateTime ;
    dct:modified "2026-05-07T22:08:48.262420"^^xsd:dateTime ;
    dct:title "Towards an Integral Approach for Modeling Causality" ;
    dcat:accessURL <https://theses.hal.science/tel-00915256> .

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<https://theses.hal.science/tel-00915256> a foaf:Document .