{"id":"https://openalex.org/W4385567744","doi":"https://doi.org/10.1145/3580305.3599859","title":"Learning to Discover Various Simpson's Paradoxes","display_name":"Learning to Discover Various Simpson's Paradoxes","publication_year":2023,"publication_date":"2023-08-04","ids":{"openalex":"https://openalex.org/W4385567744","doi":"https://doi.org/10.1145/3580305.3599859"},"language":"en","primary_location":{"id":"doi:10.1145/3580305.3599859","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3580305.3599859","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3580305.3599859","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":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://dl.acm.org/doi/pdf/10.1145/3580305.3599859","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5019219172","display_name":"Jingwei Wang","orcid":"https://orcid.org/0000-0001-6454-8102"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Jingwei Wang","raw_affiliation_strings":["Ant Group, Shanghai, China"],"affiliations":[{"raw_affiliation_string":"Ant Group, Shanghai, China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5058116213","display_name":"Jianshan He","orcid":"https://orcid.org/0000-0002-7398-3568"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jianshan He","raw_affiliation_strings":["Ant Group, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Ant Group, Beijing, China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5103257775","display_name":"Weidi Xu","orcid":"https://orcid.org/0000-0002-7279-9339"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Weidi Xu","raw_affiliation_strings":["Ant Group, Shanghai, China"],"affiliations":[{"raw_affiliation_string":"Ant Group, Shanghai, China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5075902723","display_name":"Ruopeng Li","orcid":"https://orcid.org/0000-0003-3329-2348"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ruopeng Li","raw_affiliation_strings":["Ant Group, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Ant Group, Beijing, China","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5055732061","display_name":"Wei Chu","orcid":"https://orcid.org/0000-0002-6401-6111"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wei Chu","raw_affiliation_strings":["Ant Group, Hangzhou, China"],"affiliations":[{"raw_affiliation_string":"Ant Group, Hangzhou, China","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5019219172"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.4595,"has_fulltext":true,"cited_by_count":1,"citation_normalized_percentile":{"value":0.6749025,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":94},"biblio":{"volume":null,"issue":null,"first_page":"5092","last_page":"5103"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10538","display_name":"Data Mining Algorithms and Applications","score":0.9969000220298767,"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"}},"topics":[{"id":"https://openalex.org/T10538","display_name":"Data Mining Algorithms and Applications","score":0.9969000220298767,"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/T10637","display_name":"Advanced Clustering Algorithms Research","score":0.9775999784469604,"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/T10057","display_name":"Face and Expression Recognition","score":0.9718000292778015,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/categorical-variable","display_name":"Categorical variable","score":0.7693955898284912},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6700139045715332},{"id":"https://openalex.org/keywords/perspective","display_name":"Perspective (graphical)","score":0.6204724907875061},{"id":"https://openalex.org/keywords/code","display_name":"Code (set theory)","score":0.6123805046081543},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5543215870857239},{"id":"https://openalex.org/keywords/function","display_name":"Function (biology)","score":0.49047014117240906},{"id":"https://openalex.org/keywords/variable","display_name":"Variable (mathematics)","score":0.41623345017433167},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4112902879714966},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.41028478741645813},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.2580004930496216}],"concepts":[{"id":"https://openalex.org/C5274069","wikidata":"https://www.wikidata.org/wiki/Q2285707","display_name":"Categorical variable","level":2,"score":0.7693955898284912},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6700139045715332},{"id":"https://openalex.org/C12713177","wikidata":"https://www.wikidata.org/wiki/Q1900281","display_name":"Perspective (graphical)","level":2,"score":0.6204724907875061},{"id":"https://openalex.org/C2776760102","wikidata":"https://www.wikidata.org/wiki/Q5139990","display_name":"Code (set theory)","level":3,"score":0.6123805046081543},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5543215870857239},{"id":"https://openalex.org/C14036430","wikidata":"https://www.wikidata.org/wiki/Q3736076","display_name":"Function (biology)","level":2,"score":0.49047014117240906},{"id":"https://openalex.org/C182365436","wikidata":"https://www.wikidata.org/wiki/Q50701","display_name":"Variable (mathematics)","level":2,"score":0.41623345017433167},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4112902879714966},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.41028478741645813},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2580004930496216},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C78458016","wikidata":"https://www.wikidata.org/wiki/Q840400","display_name":"Evolutionary biology","level":1,"score":0.0},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3580305.3599859","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3580305.3599859","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3580305.3599859","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"}],"best_oa_location":{"id":"doi:10.1145/3580305.3599859","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3580305.3599859","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3580305.3599859","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":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4385567744.pdf","grobid_xml":"https://content.openalex.org/works/W4385567744.grobid-xml"},"referenced_works_count":22,"referenced_works":["https://openalex.org/W109767419","https://openalex.org/W1974423219","https://openalex.org/W1982289080","https://openalex.org/W2001619934","https://openalex.org/W2020259034","https://openalex.org/W2027987815","https://openalex.org/W2048087720","https://openalex.org/W2063841934","https://openalex.org/W2095577528","https://openalex.org/W2118590195","https://openalex.org/W2151705433","https://openalex.org/W2320778555","https://openalex.org/W2529466265","https://openalex.org/W2613752415","https://openalex.org/W2905472553","https://openalex.org/W2963061618","https://openalex.org/W2964196504","https://openalex.org/W3024109520","https://openalex.org/W3025013230","https://openalex.org/W3198076800","https://openalex.org/W4288471717","https://openalex.org/W4310895557"],"related_works":["https://openalex.org/W4386799044","https://openalex.org/W2773208253","https://openalex.org/W2560646951","https://openalex.org/W4297454206","https://openalex.org/W65104662","https://openalex.org/W1871748041","https://openalex.org/W2362286668","https://openalex.org/W2133382151","https://openalex.org/W3121946870","https://openalex.org/W94371014"],"abstract_inverted_index":{"Simpson's":[0,47,93,109,167],"paradox":[1,23,48,60,110],"is":[2,180],"a":[3,20,54,87,98,101,116,139],"well-known":[4],"statistical":[5,102],"phenomenon":[6],"that":[7,104,162],"has":[8],"captured":[9],"the":[10,59,127,154],"attention":[11],"of":[12,46,108,130,133],"statisticians,":[13],"mathematicians,":[14],"and":[15,32,52,172],"philosophers":[16],"for":[17,74],"more":[18],"than":[19],"century.":[21],"The":[22,178],"often":[24],"confuses":[25],"people":[26],"when":[27],"it":[28,34],"appears":[29],"in":[30,49],"data,":[31],"ignoring":[33],"may":[35],"lead":[36],"to":[37,57,90,125,145],"incorrect":[38],"decisions.":[39],"Recent":[40],"studies":[41],"have":[42],"found":[43],"many":[44,67],"examples":[45],"social":[50],"data":[51,148],"proposed":[53],"few":[55],"methods":[56,64],"detect":[58],"automatically.":[61],"However,":[62],"these":[63,83],"suffer":[65],"from":[66,100],"limitations,":[68],"such":[69],"as":[70],"being":[71],"only":[72],"suitable":[73],"categorical":[75],"variables":[76,132],"or":[77],"one":[78],"specific":[79],"paradox.":[80],"To":[81],"address":[82],"problems,":[84],"we":[85,96,114,137],"develop":[86],"learning-based":[88],"approach":[89],"discover":[91,165],"various":[92,159,166],"paradoxes.":[94],"Firstly,":[95],"propose":[97],"framework":[99],"perspective":[103],"unifies":[105],"multiple":[106,134,150],"variants":[107],"currently":[111],"known.":[112],"Secondly,":[113],"present":[115],"novel":[117],"loss":[118],"function,":[119],"Multi-group":[120],"Pearson":[121],"Correlation":[122],"Coefficient":[123],"(MPCC),":[124],"calculate":[126],"association":[128],"strength":[129],"two":[131],"subgroups.":[135],"Then,":[136],"design":[138],"neural":[140],"network":[141],"model,":[142],"coined":[143],"SimNet,":[144],"automatically":[146],"disaggregate":[147],"into":[149],"subgroups":[151],"by":[152,170],"optimizing":[153],"MPCC":[155],"loss.":[156],"Experiments":[157],"on":[158],"datasets":[160],"demonstrate":[161],"SimNet":[163],"can":[164],"paradoxes":[168],"caused":[169],"discrete":[171],"continuous":[173],"variables,":[174],"even":[175],"hidden":[176],"variables.":[177],"code":[179],"available":[181],"at":[182],"https://github.com/ant-research/Learning-to-Discover-Various-Simpson-Paradoxes.":[183]},"counts_by_year":[{"year":2024,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}