{"id":"https://openalex.org/W4402594840","doi":"https://doi.org/10.1109/sds60720.2024.00018","title":"CIML-R: Causally Informed Machine Learning Based on Feature Relevance","display_name":"CIML-R: Causally Informed Machine Learning Based on Feature Relevance","publication_year":2024,"publication_date":"2024-05-30","ids":{"openalex":"https://openalex.org/W4402594840","doi":"https://doi.org/10.1109/sds60720.2024.00018"},"language":"en","primary_location":{"id":"doi:10.1109/sds60720.2024.00018","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/sds60720.2024.00018","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 11th IEEE Swiss Conference on Data Science (SDS)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5092045114","display_name":"Martin Surner","orcid":"https://orcid.org/0009-0007-4373-6834"},"institutions":[{"id":"https://openalex.org/I202671389","display_name":"University of Applied Sciences Landshut","ror":"https://ror.org/056z5bx32","country_code":"DE","type":"education","lineage":["https://openalex.org/I202671389"]}],"countries":["DE"],"is_corresponding":true,"raw_author_name":"Martin Surner","raw_affiliation_strings":["Landshut University of Applied Sciences,Institute for Data and Process Science,Computer Science Department,Landshut,Germany"],"affiliations":[{"raw_affiliation_string":"Landshut University of Applied Sciences,Institute for Data and Process Science,Computer Science Department,Landshut,Germany","institution_ids":["https://openalex.org/I202671389"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5065919137","display_name":"Abdelmajid Khelil","orcid":"https://orcid.org/0000-0002-4536-8058"},"institutions":[{"id":"https://openalex.org/I202671389","display_name":"University of Applied Sciences Landshut","ror":"https://ror.org/056z5bx32","country_code":"DE","type":"education","lineage":["https://openalex.org/I202671389"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Abdelmajid Khelil","raw_affiliation_strings":["Landshut University of Applied Sciences,Institute for Data and Process Science,Computer Science Department,Landshut,Germany"],"affiliations":[{"raw_affiliation_string":"Landshut University of Applied Sciences,Institute for Data and Process Science,Computer Science Department,Landshut,Germany","institution_ids":["https://openalex.org/I202671389"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5092045114"],"corresponding_institution_ids":["https://openalex.org/I202671389"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.13540251,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"23","issue":null,"first_page":"68","last_page":"75"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10320","display_name":"Neural Networks and Applications","score":0.7749000191688538,"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/T10320","display_name":"Neural Networks and Applications","score":0.7749000191688538,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7279576659202576},{"id":"https://openalex.org/keywords/relevance","display_name":"Relevance (law)","score":0.7179898023605347},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5942279696464539},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.547791063785553},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.44244688749313354}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7279576659202576},{"id":"https://openalex.org/C158154518","wikidata":"https://www.wikidata.org/wiki/Q7310970","display_name":"Relevance (law)","level":2,"score":0.7179898023605347},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5942279696464539},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.547791063785553},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.44244688749313354},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/sds60720.2024.00018","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/sds60720.2024.00018","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 11th IEEE Swiss Conference on Data Science (SDS)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":29,"referenced_works":["https://openalex.org/W1607078431","https://openalex.org/W2067906953","https://openalex.org/W2084092936","https://openalex.org/W2143891888","https://openalex.org/W2951576127","https://openalex.org/W2973136764","https://openalex.org/W3006087551","https://openalex.org/W3133542152","https://openalex.org/W3135588948","https://openalex.org/W3171209108","https://openalex.org/W3171849353","https://openalex.org/W3188805207","https://openalex.org/W3191346550","https://openalex.org/W4205511145","https://openalex.org/W4206573688","https://openalex.org/W4220858968","https://openalex.org/W4226108798","https://openalex.org/W4255871753","https://openalex.org/W4289639938","https://openalex.org/W4318185477","https://openalex.org/W4378419993","https://openalex.org/W4391753543","https://openalex.org/W6637242042","https://openalex.org/W6638926925","https://openalex.org/W6640963894","https://openalex.org/W6734194636","https://openalex.org/W6803453700","https://openalex.org/W6810123564","https://openalex.org/W6842889262"],"related_works":["https://openalex.org/W2961085424","https://openalex.org/W4306674287","https://openalex.org/W3046775127","https://openalex.org/W3107602296","https://openalex.org/W4394896187","https://openalex.org/W3170094116","https://openalex.org/W4386462264","https://openalex.org/W4364306694","https://openalex.org/W4312192474","https://openalex.org/W4283697347"],"abstract_inverted_index":{"Applications":[0],"relying":[1],"on":[2,86,152],"machine":[3],"learning":[4,7,139],"and":[5,129],"statistical":[6],"techniques,":[8],"such":[9],"as":[10,21],"neural":[11,55,72,97,126],"networks,":[12],"have":[13],"significantly":[14],"grown":[15],"over":[16],"the":[17,58,70,77,87,93,101,116,121,131,138,143,147],"past":[18],"decade.":[19],"Nevertheless,":[20],"these":[22],"techniques":[23],"learn":[24],"only":[25],"from":[26,31],"observational":[27],"data,":[28],"they":[29],"suffer":[30],"spurious":[32,80,153],"correlations":[33],"that":[34,53,137],"may":[35],"limit":[36],"their":[37],"performance":[38,122],"in":[39,111,115],"domain":[40],"shifts.":[41],"In":[42],"this":[43,47],"paper,":[44],"we":[45],"address":[46],"issue.":[48],"We":[49,119],"propose":[50],"an":[51],"approach":[52,149],"guides":[54],"networks":[56,98],"during":[57],"training":[59],"phase":[60],"using":[61],"explanations.":[62],"User-defined":[63],"causal":[64,88,144],"knowledge":[65],"is":[66,108],"incorporated":[67],"to":[68,99],"guide":[69],"considered":[71],"network.":[73],"Such":[74],"guidance":[75],"reduces":[76],"risk":[78],"of":[79,96,123],"correlations,":[81],"allowing":[82],"for":[83,110],"a":[84,112,124],"focus":[85],"structure.":[89],"Our":[90],"method":[91],"extends":[92],"feed-forward":[94],"step":[95],"track":[100],"feature":[102,106],"relevance.":[103],"The":[104],"resulting":[105],"relevance":[107],"accounted":[109],"regularization":[113],"term":[114],"loss":[117],"function.":[118],"compare":[120],"conventional":[125,148],"network":[127],"with":[128],"without":[130],"proposed":[132],"extension.":[133],"This":[134],"comparison":[135],"indicates":[136],"process":[140],"successfully":[141],"adopts":[142],"mechanism,":[145],"while":[146],"learns":[150],"based":[151],"correlations.":[154]},"counts_by_year":[],"updated_date":"2025-12-27T23:08:20.325037","created_date":"2025-10-10T00:00:00"}