乐胖代购免代理版

{"id":"https://openalex.org/W4367046883","doi":"https://doi.org/10.1145/3543507.3583280","title":"Path-specific Causal Fair Prediction via Auxiliary Graph Structure Learning","display_name":"Path-specific Causal Fair Prediction via Auxiliary Graph Structure Learning","publication_year":2023,"publication_date":"2023-04-26","ids":{"openalex":"https://openalex.org/W4367046883","doi":"https://doi.org/10.1145/3543507.3583280"},"language":"en","primary_location":{"is_oa":false,"landing_page_url":"https://doi.org/10.1145/3543507.3583280","pdf_url":null,"source":{"id":"https://openalex.org/S4363608783","display_name":"Proceedings of the ACM Web Conference 2022","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_indexed_in_scopus":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false},"type":"article","type_crossref":"proceedings-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/A5032616889","display_name":"Liuyi Yao","orcid":"https://orcid.org/0000-0003-3828-796X"},"institutions":[{"id":"https://openalex.org/I45928872","display_name":"Alibaba Group (China)","ror":"https://ror.org/00k642b80","country_code":"CN","type":"company","lineage":["https://openalex.org/I45928872"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Liuyi Yao","raw_affiliation_strings":["Alibaba Group, China"],"affiliations":[{"raw_affiliation_string":"Alibaba Group, China","institution_ids":["https://openalex.org/I45928872"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5046576694","display_name":"Yaliang Li","orcid":"https://orcid.org/0000-0002-4204-6096"},"institutions":[{"id":"https://openalex.org/I4210095624","display_name":"Alibaba Group (United States)","ror":"https://ror.org/00rn0m335","country_code":"US","type":"company","lineage":["https://openalex.org/I4210095624","https://openalex.org/I45928872"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yaliang Li","raw_affiliation_strings":["Alibaba Group, USA"],"affiliations":[{"raw_affiliation_string":"Alibaba Group, USA","institution_ids":["https://openalex.org/I4210095624"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5040297543","display_name":"Bolin Ding","orcid":"https://orcid.org/0000-0003-1535-9692"},"institutions":[{"id":"https://openalex.org/I4210095624","display_name":"Alibaba Group (United States)","ror":"https://ror.org/00rn0m335","country_code":"US","type":"company","lineage":["https://openalex.org/I4210095624","https://openalex.org/I45928872"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Bolin Ding","raw_affiliation_strings":["Alibaba Group, USA"],"affiliations":[{"raw_affiliation_string":"Alibaba Group, USA","institution_ids":["https://openalex.org/I4210095624"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5057864403","display_name":"Jingren Zhou","orcid":"https://orcid.org/0000-0002-4220-2634"},"institutions":[{"id":"https://openalex.org/I45928872","display_name":"Alibaba Group (China)","ror":"https://ror.org/00k642b80","country_code":"CN","type":"company","lineage":["https://openalex.org/I45928872"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jingren Zhou","raw_affiliation_strings":["Alibaba, China"],"affiliations":[{"raw_affiliation_string":"Alibaba, China","institution_ids":["https://openalex.org/I45928872"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5066408605","display_name":"Jinduo Liu","orcid":"https://orcid.org/0000-0002-6264-0471"},"institutions":[{"id":"https://openalex.org/I37796252","display_name":"Beijing University of Technology","ror":"https://ror.org/037b1pp87","country_code":"CN","type":"funder","lineage":["https://openalex.org/I37796252"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jinduo Liu","raw_affiliation_strings":["Beijing University of Technology, China"],"affiliations":[{"raw_affiliation_string":"Beijing University of Technology, China","institution_ids":["https://openalex.org/I37796252"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5016035883","display_name":"Mengdi Huai","orcid":"https://orcid.org/0000-0001-6368-5973"},"institutions":[{"id":"https://openalex.org/I173911158","display_name":"Iowa State University","ror":"https://ror.org/04rswrd78","country_code":"US","type":"funder","lineage":["https://openalex.org/I173911158"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Mengdi Huai","raw_affiliation_strings":["Iowa State University, USA"],"affiliations":[{"raw_affiliation_string":"Iowa State University, USA","institution_ids":["https://openalex.org/I173911158"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5068042288","display_name":"Jing Gao","orcid":"https://orcid.org/0000-0002-1557-7553"},"institutions":[{"id":"https://openalex.org/I219193219","display_name":"Purdue University West Lafayette","ror":"https://ror.org/02dqehb95","country_code":"US","type":"funder","lineage":["https://openalex.org/I219193219"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jing Gao","raw_affiliation_strings":["Purdue University, USA"],"affiliations":[{"raw_affiliation_string":"Purdue University, USA","institution_ids":["https://openalex.org/I219193219"]}]}],"institution_assertions":[],"countries_distinct_count":2,"institutions_distinct_count":5,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.0,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":0,"max":65},"biblio":{"volume":null,"issue":null,"first_page":"3680","last_page":"3688"},"is_retracted":false,"is_paratext":false,"primary_topic":{"id":"https://openalex.org/T10883","display_name":"Ethics and Social Impacts of AI","score":0.9812,"subfield":{"id":"https://openalex.org/subfields/3311","display_name":"Safety Research"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},"topics":[{"id":"https://openalex.org/T10883","display_name":"Ethics and Social Impacts of AI","score":0.9812,"subfield":{"id":"https://openalex.org/subfields/3311","display_name":"Safety Research"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T10764","display_name":"Privacy-Preserving Technologies in Data","score":0.9365,"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/T11631","display_name":"Retirement, Disability, and Employment","score":0.9315,"subfield":{"id":"https://openalex.org/subfields/3317","display_name":"Demography"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7672395},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.6062421},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.51655614},{"id":"https://openalex.org/C2777735758","wikidata":"https://www.wikidata.org/wiki/Q817765","display_name":"Path (computing)","level":2,"score":0.5024078},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.48385966},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.4794237},{"id":"https://openalex.org/C557471498","wikidata":"https://www.wikidata.org/wiki/Q554950","display_name":"Recommender system","level":2,"score":0.43105108},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.42743242},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3425058},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.14100695},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"is_oa":false,"landing_page_url":"https://doi.org/10.1145/3543507.3583280","pdf_url":null,"source":{"id":"https://openalex.org/S4363608783","display_name":"Proceedings of the ACM Web Conference 2022","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_indexed_in_scopus":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.4,"id":"https://metadata.un.org/sdg/10","display_name":"Reduced inequalities"}],"grants":[],"datasets":[],"versions":[],"referenced_works_count":17,"referenced_works":["https://openalex.org/W2054141820","https://openalex.org/W2550530154","https://openalex.org/W2605350416","https://openalex.org/W2616536620","https://openalex.org/W2788651580","https://openalex.org/W2897363137","https://openalex.org/W2909212904","https://openalex.org/W2962787423","https://openalex.org/W2963331808","https://openalex.org/W2964060106","https://openalex.org/W2965548693","https://openalex.org/W2966613548","https://openalex.org/W3021010086","https://openalex.org/W3037831233","https://openalex.org/W3116873649","https://openalex.org/W3156939347","https://openalex.org/W3160537436"],"related_works":["https://openalex.org/W4390273403","https://openalex.org/W4386781444","https://openalex.org/W4317039510","https://openalex.org/W4288390103","https://openalex.org/W4238861846","https://openalex.org/W3197542405","https://openalex.org/W3125580266","https://openalex.org/W3092950680","https://openalex.org/W2150182025","https://openalex.org/W2056712470"],"abstract_inverted_index":{"With":[0],"ubiquitous":[1],"adoption":[2],"of":[3,86,178],"machine":[4],"learning":[5,107],"algorithms":[6],"in":[7,83,118,147],"web":[8],"technologies,":[9],"such":[10,150],"as":[11,48,75,151],"recommendation":[12,152],"system":[13],"and":[14,24,53,138,174,194],"social":[15,31],"network,":[16],"algorithm":[17,62,195],"fairness":[18,35,39,70,196],"has":[19,26],"become":[20],"a":[21,27,41,95,176],"trending":[22],"topic,":[23],"it":[25,49],"great":[28,46],"impact":[29],"on":[30,61,67,164,180],"welfare.":[32],"Among":[33],"different":[34],"definitions,":[36],"path-specific":[37,68,87],"causal":[38,69,120],"is":[40,144],"widely":[42],"adopted":[43],"one":[44],"with":[45],"potentials,":[47],"distinguishes":[50],"the":[51,57,76,84,119,125,129,165,169,186],"fair":[52,100,109,171],"unfair":[54,114],"effects":[55],"that":[56,113,185],"sensitive":[58,132],"attributes":[59,133],"exert":[60],"predictions.":[63],"Existing":[64],"methods":[65],"based":[66,99],"either":[71],"require":[72],"graph":[73,98,105],"structure":[74,106],"prior":[77],"knowledge":[78],"or":[79],"have":[80],"high":[81],"complexity":[82],"calculation":[85],"effect.":[88],"To":[89],"tackle":[90],"these":[91],"challenges,":[92],"we":[93,123],"propose":[94],"novel":[96],"casual":[97],"prediction":[101,110,172,192],"framework":[102,127,188],"which":[103,143],"integrates":[104],"into":[108],"to":[111,128,183],"ensure":[112],"pathways":[115],"are":[116],"excluded":[117],"graph.":[121],"Furthermore,":[122],"generalize":[124],"proposed":[126,170,187],"scenarios":[130],"where":[131],"can":[134,189],"be":[135],"non-root":[136],"nodes":[137],"affected":[139],"by":[140,157],"other":[141],"variables,":[142],"commonly":[145],"observed":[146],"real-world":[148,181],"applications,":[149],"system,":[153],"but":[154],"hardly":[155],"addressed":[156],"existing":[158],"works.":[159],"We":[160],"provide":[161,190],"theoretical":[162],"analysis":[163],"generalization":[166],"bound":[167],"for":[168],"method,":[173],"conduct":[175],"series":[177],"experiments":[179],"datasets":[182],"demonstrate":[184],"better":[191],"performance":[193],"trade-off.":[197]},"abstract_inverted_index_v3":null,"cited_by_api_url":"https://api.openalex.org/works?filter=cites:W4367046883","counts_by_year":[],"updated_date":"2025-04-13T00:20:55.638464","created_date":"2023-04-27"}