{"id":"https://openalex.org/W4402993337","doi":"https://doi.org/10.3233/faia240404","title":"Unsupervised Pairwise Causal Discovery on Heterogeneous Data Using Mutual Information Measures","display_name":"Unsupervised Pairwise Causal Discovery on Heterogeneous Data Using Mutual Information Measures","publication_year":2024,"publication_date":"2024-09-25","ids":{"openalex":"https://openalex.org/W4402993337","doi":"https://doi.org/10.3233/faia240404"},"language":"en","primary_location":{"id":"doi:10.3233/faia240404","doi":"https://doi.org/10.3233/faia240404","is_oa":true,"landing_page_url":"http://dx.doi.org/10.3233/faia240404","pdf_url":null,"source":{"id":"https://openalex.org/S4210201731","display_name":"Frontiers in artificial intelligence and applications","issn_l":"0922-6389","issn":["0922-6389","1879-8314"],"is_oa":false,"is_in_doaj":false,"is_core":true,"listed_in":["cwts-core","jufo-1","norway-1"],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by-nc","license_id":"https://openalex.org/licenses/cc-by-nc","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Frontiers in Artificial Intelligence and Applications","raw_type":"book-chapter"},"volume":{"id":"https://openalex.org/W4402993577","display_name":"Artificial Intelligence Research and Development"},"type":"book-chapter","is_traditional":true,"indexed_in":["arxiv","crossref","datacite"],"open_access":{"is_oa":true,"oa_status":"hybrid","oa_url":"http://dx.doi.org/10.3233/faia240404","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5018996294","display_name":"Alexandre Trilla","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I4210093562","display_name":"Alstom (Spain)","ror":"https://ror.org/00rffka72","country_code":"ES","type":"company","lineage":["https://openalex.org/I36169673","https://openalex.org/I4210093562"]}],"countries":["ES"],"is_corresponding":false,"raw_author_name":"Alexandre Trilla","raw_affiliation_strings":["Alstom, Santa Perp\u00e8tua de la Mogoda, Barcelona, 08130, Spain"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Alstom, Santa Perp\u00e8tua de la Mogoda, Barcelona, 08130, Spain","institution_ids":["https://openalex.org/I4210093562"]}]},{"author_position":"last","author":{"id":null,"display_name":"Nenad Mijatovic","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I36169673","display_name":"Alstom (France)","ror":"https://ror.org/00t4db855","country_code":"FR","type":"company","lineage":["https://openalex.org/I36169673"]}],"countries":["FR"],"is_corresponding":false,"raw_author_name":"Nenad Mijatovic","raw_affiliation_strings":["Alstom, Saint Ouen, Paris, 93482, France"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Alstom, Saint Ouen, Paris, 93482, France","institution_ids":["https://openalex.org/I36169673"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.32822698,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11303","display_name":"Bayesian Modeling and Causal Inference","score":0.9994999766349792,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T11303","display_name":"Bayesian Modeling and Causal Inference","score":0.9994999766349792,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10538","display_name":"Data Mining Algorithms and Applications","score":9.999999747378752e-05,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10778","display_name":"Philosophy of Biology and Scientific Explanation","score":0.0,"subfield":{"id":"https://openalex.org/subfields/1207","display_name":"History and Philosophy of Science"},"field":{"id":"https://openalex.org/fields/12","display_name":"Arts and Humanities"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/causal-discovery","display_name":"causal discovery","score":0.9660000205039978},{"id":"https://openalex.org/keywords/mutual-information","display_name":"mutual information","score":0.6809999942779541},{"id":"https://openalex.org/keywords/unsupervised-learning","display_name":"unsupervised learning","score":0.8650000095367432},{"id":"https://openalex.org/keywords/data-heterogeneity","display_name":"data heterogeneity","score":0.9210000038146973},{"id":"https://openalex.org/keywords/variable-types","display_name":"variable types","score":0.7210000157356262},{"id":"https://openalex.org/keywords/causal-inference","display_name":"causal inference","score":0.421999990940094},{"id":"https://openalex.org/keywords/generalizability","display_name":"generalizability","score":0.5389999747276306}],"concepts":[{"id":"https://openalex.org/C184898388","wikidata":"https://www.wikidata.org/wiki/Q1435712","display_name":"Pairwise comparison","level":2,"score":0.8362922668457031},{"id":"https://openalex.org/C152139883","wikidata":"https://www.wikidata.org/wiki/Q252973","display_name":"Mutual information","level":2,"score":0.5637949705123901},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.521605372428894},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4322141110897064},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.34826362133026123},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.33498406410217285}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.3233/faia240404","doi":"https://doi.org/10.3233/faia240404","is_oa":true,"landing_page_url":"http://dx.doi.org/10.3233/faia240404","pdf_url":null,"source":{"id":"https://openalex.org/S4210201731","display_name":"Frontiers in artificial intelligence and applications","issn_l":"0922-6389","issn":["0922-6389","1879-8314"],"is_oa":false,"is_in_doaj":false,"is_core":true,"listed_in":["cwts-core","jufo-1","norway-1"],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by-nc","license_id":"https://openalex.org/licenses/cc-by-nc","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Frontiers in Artificial Intelligence and Applications","raw_type":"book-chapter"},{"id":"pmh:oai:arXiv.org:2408.00399","doi":null,"is_oa":true,"landing_page_url":"http://arxiv.org/abs/2408.00399","pdf_url":"https://arxiv.org/pdf/2408.00399","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"doi:10.48550/arxiv.2408.00399","doi":"https://doi.org/10.48550/arxiv.2408.00399","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2408.00399","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.3233/faia240404","doi":"https://doi.org/10.3233/faia240404","is_oa":true,"landing_page_url":"http://dx.doi.org/10.3233/faia240404","pdf_url":null,"source":{"id":"https://openalex.org/S4210201731","display_name":"Frontiers in artificial intelligence and applications","issn_l":"0922-6389","issn":["0922-6389","1879-8314"],"is_oa":false,"is_in_doaj":false,"is_core":true,"listed_in":["cwts-core","jufo-1","norway-1"],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by-nc","license_id":"https://openalex.org/licenses/cc-by-nc","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Frontiers in Artificial Intelligence and Applications","raw_type":"book-chapter"},"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4402993337.pdf","grobid_xml":"https://content.openalex.org/works/W4402993337.grobid-xml"},"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W4412945520","https://openalex.org/W6926252337","https://openalex.org/W2790618411","https://openalex.org/W235810693","https://openalex.org/W4376312458","https://openalex.org/W224141410","https://openalex.org/W4313181991","https://openalex.org/W4220688463","https://openalex.org/W3171231408","https://openalex.org/W7054942342"],"abstract_inverted_index":{"A":[0],"fundamental":[1],"task":[2],"in":[3,34,119,141,167],"science":[4],"is":[5,14,43,105,138],"to":[6,23,108,162],"determine":[7],"the":[8,15,24,31,35,52,56,64,67,81,88,95,130,133,142],"underlying":[9],"causal":[10],"relations":[11],"because":[12],"it":[13],"knowledge":[16],"of":[17,27,55,66,132,144,152],"this":[18,39,48,60,109,117],"functional":[19],"structure":[20],"what":[21],"leads":[22],"correct":[25],"interpretation":[26],"an":[28,120],"effect":[29],"given":[30],"apparent":[32],"associations":[33],"observed":[36],"data.":[37],"In":[38,59,113],"sense,":[40],"Causal":[41],"Discovery":[42],"a":[44,72,149,160],"technique":[45],"that":[46,75,97,156],"tackles":[47],"challenge":[49],"by":[50,70],"analyzing":[51],"statistical":[53],"properties":[54],"constituent":[57],"variables.":[58],"work,":[61],"we":[62,115,147],"target":[63],"generalizability":[65],"discovery":[68,165],"method":[69],"following":[71],"reductionist":[73],"approach":[74,116],"only":[76],"involves":[77],"two":[78],"variables,":[79],"i.e.,":[80],"pairwise":[82],"or":[83],"bi-variate":[84],"setting.":[85],"We":[86],"question":[87],"current":[89],"(possibly":[90],"misleading)":[91],"baseline":[92],"results":[93,155],"on":[94],"basis":[96],"they":[98],"were":[99],"obtained":[100],"through":[101],"supervised":[102],"learning,":[103],"which":[104,137],"arguably":[106],"contrary":[107],"genuinely":[110],"exploratory":[111],"endeavor.":[112],"consequence,":[114],"problem":[118],"unsupervised":[121],"way,":[122],"using":[123],"robust":[124],"Mutual":[125],"Information":[126],"measures,":[127],"and":[128],"observing":[129],"impact":[131],"different":[134],"variable":[135],"types,":[136],"oftentimes":[139],"ignored":[140],"design":[143],"solutions.":[145],"Thus,":[146],"provide":[148],"novel":[150],"set":[151],"standard":[153],"unbiased":[154],"can":[157],"serve":[158],"as":[159],"reference":[161],"guide":[163],"future":[164],"tasks":[166],"completely":[168],"unknown":[169],"environments.":[170]},"counts_by_year":[],"updated_date":"2026-11-04T10:54:05.112534","created_date":"2024-10-01T00:00:00"}