{"id":"https://openalex.org/W4384644679","doi":"https://doi.org/10.1145/3580305.3599408","title":"Learning for Counterfactual Fairness from Observational Data","display_name":"Learning for Counterfactual Fairness from Observational Data","publication_year":2023,"publication_date":"2023-08-04","ids":{"openalex":"https://openalex.org/W4384644679","doi":"https://doi.org/10.1145/3580305.3599408"},"language":"en","primary_location":{"id":"doi:10.1145/3580305.3599408","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3580305.3599408","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3580305.3599408","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","raw_type":"proceedings-article"},"type":"article","indexed_in":["arxiv","crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://dl.acm.org/doi/pdf/10.1145/3580305.3599408","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5032200312","display_name":"Jing Ma","orcid":"https://orcid.org/0000-0003-4237-6607"},"institutions":[{"id":"https://openalex.org/I51556381","display_name":"University of Virginia","ror":"https://ror.org/0153tk833","country_code":"US","type":"education","lineage":["https://openalex.org/I51556381"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Jing Ma","raw_affiliation_strings":["University of Virginia, Charlottesville, USA"],"affiliations":[{"raw_affiliation_string":"University of Virginia, Charlottesville, USA","institution_ids":["https://openalex.org/I51556381"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5054719216","display_name":"Ruocheng Guo","orcid":"https://orcid.org/0000-0002-8522-6142"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ruocheng Guo","raw_affiliation_strings":["Bytedance Research, London, United Kingdom"],"affiliations":[{"raw_affiliation_string":"Bytedance Research, London, United Kingdom","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5013588572","display_name":"Aidong Zhang","orcid":"https://orcid.org/0000-0001-9723-3246"},"institutions":[{"id":"https://openalex.org/I51556381","display_name":"University of Virginia","ror":"https://ror.org/0153tk833","country_code":"US","type":"education","lineage":["https://openalex.org/I51556381"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Aidong Zhang","raw_affiliation_strings":["University of Virginia, Charlottesville, USA"],"affiliations":[{"raw_affiliation_string":"University of Virginia, Charlottesville, USA","institution_ids":["https://openalex.org/I51556381"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5029588473","display_name":"Jundong Li","orcid":"https://orcid.org/0000-0002-1878-817X"},"institutions":[{"id":"https://openalex.org/I51556381","display_name":"University of Virginia","ror":"https://ror.org/0153tk833","country_code":"US","type":"education","lineage":["https://openalex.org/I51556381"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jundong Li","raw_affiliation_strings":["University of Virginia, Charlottesville, USA"],"affiliations":[{"raw_affiliation_string":"University of Virginia, Charlottesville, USA","institution_ids":["https://openalex.org/I51556381"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5032200312"],"corresponding_institution_ids":["https://openalex.org/I51556381"],"apc_list":null,"apc_paid":null,"fwci":2.0561,"has_fulltext":true,"cited_by_count":8,"citation_normalized_percentile":{"value":0.8924203,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":96,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"1620","last_page":"1630"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10883","display_name":"Ethics and Social Impacts of AI","score":0.9990000128746033,"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":0.9990000128746033,"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/T12026","display_name":"Explainable Artificial Intelligence (XAI)","score":0.9592000246047974,"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/T10764","display_name":"Privacy-Preserving Technologies in Data","score":0.9106000065803528,"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/counterfactual-thinking","display_name":"Counterfactual thinking","score":0.9712968468666077},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7390583753585815},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5824121832847595},{"id":"https://openalex.org/keywords/counterfactual-conditional","display_name":"Counterfactual conditional","score":0.5378351211547852},{"id":"https://openalex.org/keywords/observational-study","display_name":"Observational study","score":0.5365870594978333},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5303300619125366},{"id":"https://openalex.org/keywords/causal-model","display_name":"Causal model","score":0.5287900567054749},{"id":"https://openalex.org/keywords/perspective","display_name":"Perspective (graphical)","score":0.4490974545478821},{"id":"https://openalex.org/keywords/data-science","display_name":"Data science","score":0.3392797112464905},{"id":"https://openalex.org/keywords/psychology","display_name":"Psychology","score":0.17205268144607544},{"id":"https://openalex.org/keywords/social-psychology","display_name":"Social psychology","score":0.13080918788909912},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.1147250235080719},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.09396997094154358}],"concepts":[{"id":"https://openalex.org/C108650721","wikidata":"https://www.wikidata.org/wiki/Q1783253","display_name":"Counterfactual thinking","level":2,"score":0.9712968468666077},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7390583753585815},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5824121832847595},{"id":"https://openalex.org/C71889745","wikidata":"https://www.wikidata.org/wiki/Q1783264","display_name":"Counterfactual conditional","level":3,"score":0.5378351211547852},{"id":"https://openalex.org/C23131810","wikidata":"https://www.wikidata.org/wiki/Q818574","display_name":"Observational study","level":2,"score":0.5365870594978333},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5303300619125366},{"id":"https://openalex.org/C11671645","wikidata":"https://www.wikidata.org/wiki/Q5054567","display_name":"Causal model","level":2,"score":0.5287900567054749},{"id":"https://openalex.org/C12713177","wikidata":"https://www.wikidata.org/wiki/Q1900281","display_name":"Perspective (graphical)","level":2,"score":0.4490974545478821},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.3392797112464905},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.17205268144607544},{"id":"https://openalex.org/C77805123","wikidata":"https://www.wikidata.org/wiki/Q161272","display_name":"Social psychology","level":1,"score":0.13080918788909912},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.1147250235080719},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.09396997094154358}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1145/3580305.3599408","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3580305.3599408","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3580305.3599408","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:2307.08232","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2307.08232","pdf_url":"https://arxiv.org/pdf/2307.08232","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"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":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"}],"best_oa_location":{"id":"doi:10.1145/3580305.3599408","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3580305.3599408","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3580305.3599408","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G1291062413","display_name":null,"funder_award_id":"IIS-1955151","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G1378635745","display_name":null,"funder_award_id":"IIS-2008208","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G2167990707","display_name":null,"funder_award_id":"1955151","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G2522669024","display_name":"Collaborative Research: CCRI: New: A Scalable Hardware and Software Environment Enabling Secure Multi-party Learning","funder_award_id":"2213700","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G3787234217","display_name":"Collaborative Research: PPoSS: LARGE: Co-designing Hardware, Software, and Algorithms to Enable Extreme-Scale Machine Learning Systems","funder_award_id":"2217071","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G489220452","display_name":"Collaborative Research: III: Small: Graph-Oriented Usable Interpretation","funder_award_id":"2223769","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G4932945845","display_name":"CAREER: Toward A Knowledge-Guided Framework for Personalized Decision Making","funder_award_id":"2144209","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G5532833104","display_name":null,"funder_award_id":"IIS-1955151, IIS-2006844, IIS-2008208, IIS-2106913, IIS-2144209, IIS-2223769, CNS-2154962, CNS-2213700, BCS-2228534, and CCF-2217071","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G6014851183","display_name":null,"funder_award_id":"2008208","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G6625619449","display_name":null,"funder_award_id":"CCF-22170","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G6755165505","display_name":null,"funder_award_id":"award","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G7344714104","display_name":"Collaborative Research: SAI-R: Dynamical Coupling of Physical and Social Infrastructures: Evaluating the Impacts of Social Capital on Access to Safe Well Water","funder_award_id":"2228534","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G7378401224","display_name":"SaTC: CORE: Small: Empowering Network Attack Detection with Complex Graph Modeling","funder_award_id":"2154962","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G7378744750","display_name":null,"funder_award_id":"2006844","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G7826493304","display_name":null,"funder_award_id":"IIS-2106913","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G783310841","display_name":"III: Medium: Knowledge-Guided Meta Learning for Multi-Omics Survival Analysis","funder_award_id":"2106913","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G848032724","display_name":null,"funder_award_id":"Science","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"}],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"},{"id":"https://openalex.org/F4320307791","display_name":"Cisco Systems","ror":"https://ror.org/03yt1ez60"},{"id":"https://openalex.org/F4320338382","display_name":"Thomas Jefferson National Accelerator Facility","ror":"https://ror.org/02vwzrd76"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4384644679.pdf","grobid_xml":"https://content.openalex.org/works/W4384644679.grobid-xml"},"referenced_works_count":21,"referenced_works":["https://openalex.org/W1994407253","https://openalex.org/W2143117649","https://openalex.org/W2165582599","https://openalex.org/W2396961413","https://openalex.org/W2541678333","https://openalex.org/W2599025709","https://openalex.org/W2795096213","https://openalex.org/W2897955056","https://openalex.org/W2908623803","https://openalex.org/W2944929477","https://openalex.org/W2950173087","https://openalex.org/W2952150717","https://openalex.org/W2965548693","https://openalex.org/W2966613548","https://openalex.org/W2974817986","https://openalex.org/W2981623184","https://openalex.org/W3103891807","https://openalex.org/W3110081891","https://openalex.org/W3153432523","https://openalex.org/W3157133158","https://openalex.org/W3182127371"],"related_works":["https://openalex.org/W2056582926","https://openalex.org/W3137864021","https://openalex.org/W2162910442","https://openalex.org/W2079879923","https://openalex.org/W4200271736","https://openalex.org/W2104420793","https://openalex.org/W3017854570","https://openalex.org/W2028689793","https://openalex.org/W4243804444","https://openalex.org/W4280530714"],"abstract_inverted_index":{"Fairness-aware":[0,25],"machine":[1,26],"learning":[2,27,33,222],"has":[3],"attracted":[4],"a":[5,58,63,71,201,220],"surge":[6],"of":[7,32,70,77,94,114,188,244],"attention":[8],"in":[9,22,80,86,90,122,246],"many":[10,51],"domains,":[11],"such":[12,44,134],"as":[13,45,165],"online":[14],"advertising,":[15],"personalized":[16],"recommendation,":[17],"and":[18,48,84,132,160,176,229,238,250],"social":[19],"media":[20],"analysis":[21],"web":[23],"applications.":[24],"aims":[28],"to":[29,105,147,173,178],"eliminate":[30],"biases":[31,172,214],"models":[34,152,168,198],"against":[35],"certain":[36,40,207],"subgroups":[37],"described":[38],"by":[39,73,199],"protected":[41],"(sensitive)":[42],"attributes":[43],"race,":[46],"gender,":[47],"age.":[49],"Among":[50],"existing":[52,103],"fairness":[53,56,69,108,249],"notions,":[54],"counterfactual":[55,88,107,226,248],"is":[57,98,109,129,145],"popular":[59],"notion":[60],"defined":[61],"from":[62,154,192,215],"causal":[64,116,127,151,162,167,197],"perspective.":[65],"It":[66],"measures":[67],"the":[68,75,81,87,92,95,110,115,119,125,150,174,186,213,216,242],"predictor":[72,175],"comparing":[74],"prediction":[76,191,251],"each":[78],"individual":[79],"original":[82],"world":[83],"that":[85],"worlds":[89],"which":[91],"value":[93],"sensitive":[96,217],"attribute":[97,218],"modified.":[99],"A":[100],"prerequisite":[101],"for":[102,118],"methods":[104],"achieve":[106],"prior":[111],"human":[112,135],"knowledge":[113,136],"model":[117,128],"data.":[120],"However,":[121],"real-world":[123,239],"scenarios,":[124,143],"underlying":[126],"often":[130],"unknown,":[131],"acquiring":[133],"could":[137],"be":[138],"very":[139],"difficult.":[140],"In":[141,181],"these":[142],"it":[144],"risky":[146],"directly":[148],"trust":[149],"obtained":[153],"information":[155],"sources":[156],"with":[157,219],"unknown":[158],"reliability":[159],"even":[161],"discovery":[163],"methods,":[164],"incorrect":[166],"can":[169],"consequently":[170],"bring":[171],"lead":[177],"unfair":[179],"predictions.":[180],"this":[182],"work,":[183],"we":[184],"address":[185],"problem":[187],"counterfactually":[189],"fair":[190],"observational":[193],"data":[194,227],"without":[195],"given":[196],"proposing":[200],"novel":[202],"framework":[203,223],"CLAIRE.":[204],"Specifically,":[205],"under":[206],"general":[208],"assumptions,":[209],"CLAIRE":[210,245],"effectively":[211],"mitigates":[212],"representation":[221],"based":[224],"on":[225,235],"augmentation":[228],"an":[230],"invariant":[231],"penalty.":[232],"Experiments":[233],"conducted":[234],"both":[236,247],"synthetic":[237],"datasets":[240],"validate":[241],"superiority":[243],"performance.":[252]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":4},{"year":2024,"cited_by_count":3}],"updated_date":"2026-04-10T15:06:20.359241","created_date":"2025-10-10T00:00:00"}
