{"id":"https://openalex.org/W4401863404","doi":"https://doi.org/10.1145/3637528.3671782","title":"Self-Distilled Disentangled Learning for Counterfactual Prediction","display_name":"Self-Distilled Disentangled Learning for Counterfactual Prediction","publication_year":2024,"publication_date":"2024-08-24","ids":{"openalex":"https://openalex.org/W4401863404","doi":"https://doi.org/10.1145/3637528.3671782"},"language":"en","primary_location":{"id":"doi:10.1145/3637528.3671782","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3637528.3671782","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3637528.3671782","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://dl.acm.org/doi/pdf/10.1145/3637528.3671782","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5102373522","display_name":"Xinshu Li","orcid":"https://orcid.org/0000-0002-1202-3993"},"institutions":[{"id":"https://openalex.org/I31746571","display_name":"UNSW Sydney","ror":"https://ror.org/03r8z3t63","country_code":"AU","type":"education","lineage":["https://openalex.org/I31746571"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Xinshu Li","raw_affiliation_strings":["The University of New South Wales, Sydney, Australia"],"raw_orcid":"https://orcid.org/0000-0002-1202-3993","affiliations":[{"raw_affiliation_string":"The University of New South Wales, Sydney, Australia","institution_ids":["https://openalex.org/I31746571"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102023771","display_name":"Mingming Gong","orcid":"https://orcid.org/0000-0001-7147-5589"},"institutions":[{"id":"https://openalex.org/I165779595","display_name":"The University of Melbourne","ror":"https://ror.org/01ej9dk98","country_code":"AU","type":"education","lineage":["https://openalex.org/I165779595"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Mingming Gong","raw_affiliation_strings":["The University of Melbourne & MBZUAI, Melbourne, Australia"],"raw_orcid":"https://orcid.org/0000-0001-7147-5589","affiliations":[{"raw_affiliation_string":"The University of Melbourne & MBZUAI, Melbourne, Australia","institution_ids":["https://openalex.org/I165779595"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5052731721","display_name":"Lina Yao","orcid":"https://orcid.org/0000-0002-4149-839X"},"institutions":[{"id":"https://openalex.org/I1292875679","display_name":"Commonwealth Scientific and Industrial Research Organisation","ror":"https://ror.org/03qn8fb07","country_code":"AU","type":"government","lineage":["https://openalex.org/I1292875679","https://openalex.org/I2801453606","https://openalex.org/I4387156119"]},{"id":"https://openalex.org/I31746571","display_name":"UNSW Sydney","ror":"https://ror.org/03r8z3t63","country_code":"AU","type":"education","lineage":["https://openalex.org/I31746571"]},{"id":"https://openalex.org/I42894916","display_name":"Data61","ror":"https://ror.org/03q397159","country_code":"AU","type":"other","lineage":["https://openalex.org/I1292875679","https://openalex.org/I2801453606","https://openalex.org/I42894916","https://openalex.org/I4387156119"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Lina Yao","raw_affiliation_strings":["CSIRO's Data61 & The University of New South Wales, Sydney, Australia"],"raw_orcid":"https://orcid.org/0000-0002-4149-839X","affiliations":[{"raw_affiliation_string":"CSIRO's Data61 & The University of New South Wales, Sydney, Australia","institution_ids":["https://openalex.org/I1292875679","https://openalex.org/I31746571","https://openalex.org/I42894916"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.9755,"has_fulltext":true,"cited_by_count":3,"citation_normalized_percentile":{"value":0.76658763,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"1667","last_page":"1678"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.9980000257492065,"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/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.9980000257492065,"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/T10136","display_name":"Statistical Methods and Inference","score":0.9818999767303467,"subfield":{"id":"https://openalex.org/subfields/2613","display_name":"Statistics and Probability"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.979200005531311,"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":0.8765950202941895},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6369873285293579},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4168233275413513},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3237297534942627},{"id":"https://openalex.org/keywords/psychology","display_name":"Psychology","score":0.09594330191612244}],"concepts":[{"id":"https://openalex.org/C108650721","wikidata":"https://www.wikidata.org/wiki/Q1783253","display_name":"Counterfactual thinking","level":2,"score":0.8765950202941895},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6369873285293579},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4168233275413513},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3237297534942627},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.09594330191612244},{"id":"https://openalex.org/C77805123","wikidata":"https://www.wikidata.org/wiki/Q161272","display_name":"Social psychology","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3637528.3671782","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3637528.3671782","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3637528.3671782","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1145/3637528.3671782","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3637528.3671782","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3637528.3671782","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","raw_type":"proceedings-article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/10","score":0.5199999809265137,"display_name":"Reduced inequalities"}],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4401863404.pdf","grobid_xml":"https://content.openalex.org/works/W4401863404.grobid-xml"},"referenced_works_count":21,"referenced_works":["https://openalex.org/W2126292488","https://openalex.org/W2150291618","https://openalex.org/W2753738274","https://openalex.org/W2891765548","https://openalex.org/W2951225967","https://openalex.org/W2983831776","https://openalex.org/W3003921389","https://openalex.org/W3121556564","https://openalex.org/W3124133911","https://openalex.org/W3124658416","https://openalex.org/W3125459412","https://openalex.org/W3175627986","https://openalex.org/W3208064955","https://openalex.org/W4281777295","https://openalex.org/W4297573330","https://openalex.org/W4297781847","https://openalex.org/W4312374284","https://openalex.org/W4318830297","https://openalex.org/W4393147317","https://openalex.org/W6675970015","https://openalex.org/W6779019920"],"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":{"The":[0],"advancements":[1],"in":[2,44,68,103,107],"disentangled":[3,76],"representation":[4],"learning":[5,47],"significantly":[6],"enhance":[7],"the":[8,29,59,98,108],"accuracy":[9],"of":[10,32,100,110],"counterfactual":[11,105],"predictions":[12],"by":[13],"granting":[14],"precise":[15],"control":[16],"over":[17],"instrumental":[18],"variables,":[19],"confounders,":[20],"and":[21,94,113],"adjustable":[22],"variables.":[23],"An":[24],"appealing":[25],"method":[26],"for":[27,84],"achieving":[28],"independent":[30,75],"separation":[31],"these":[33],"factors":[34],"is":[35],"mutual":[36,80],"information":[37,69,81],"minimization,":[38],"a":[39],"task":[40],"that":[41],"presents":[42],"challenges":[43],"numerous":[45],"machine":[46],"scenarios,":[48],"especially":[49],"within":[50],"high-dimensional":[51,85],"spaces.":[52],"To":[53],"circumvent":[54],"this":[55],"challenge,":[56],"we":[57],"propose":[58],"Self-Distilled":[60],"Disentanglement":[61],"framework,":[62],"referred":[63],"to":[64],"as":[65],"SD2.":[66],"Grounded":[67],"theory,":[70],"it":[71],"ensures":[72],"theoretically":[73],"sound":[74],"representations":[77],"without":[78],"intricate":[79],"estimator":[82],"designs":[83],"representations.":[86],"Our":[87],"comprehensive":[88],"experiments,":[89],"conducted":[90],"on":[91],"both":[92,111],"synthetic":[93],"real-world":[95],"datasets,":[96],"confirms":[97],"effectiveness":[99],"our":[101],"approach":[102],"facilitating":[104],"inference":[106],"presence":[109],"observed":[112],"unobserved":[114],"confounders.":[115]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":2}],"updated_date":"2026-08-21T09:56:20.448147","created_date":"2025-10-10T00:00:00"}