{"id":"https://openalex.org/W4413979802","doi":"https://doi.org/10.14778/3749646.3749684","title":"Suna: Scalable Causal Confounder Discovery over Relational Data","display_name":"Suna: Scalable Causal Confounder Discovery over Relational Data","publication_year":2025,"publication_date":"2025-07-01","ids":{"openalex":"https://openalex.org/W4413979802","doi":"https://doi.org/10.14778/3749646.3749684"},"language":"en","primary_location":{"id":"doi:10.14778/3749646.3749684","is_oa":false,"landing_page_url":"https://doi.org/10.14778/3749646.3749684","pdf_url":null,"source":{"id":"https://openalex.org/S4210226185","display_name":"Proceedings of the VLDB Endowment","issn_l":"2150-8097","issn":["2150-8097"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319798","host_organization_name":"Association for Computing Machinery","host_organization_lineage":["https://openalex.org/P4310319798"],"host_organization_lineage_names":["Association for Computing Machinery"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the VLDB Endowment","raw_type":"journal-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/A5048450551","display_name":"Jiaxiang Liu","orcid":null},"institutions":[{"id":"https://openalex.org/I78577930","display_name":"Columbia University","ror":"https://ror.org/00hj8s172","country_code":"US","type":"education","lineage":["https://openalex.org/I78577930"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jiaxiang Liu","raw_affiliation_strings":["Columbia University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Columbia University","institution_ids":["https://openalex.org/I78577930"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5089331122","display_name":"Siyuan Xia","orcid":null},"institutions":[{"id":"https://openalex.org/I40347166","display_name":"University of Chicago","ror":"https://ror.org/024mw5h28","country_code":"US","type":"education","lineage":["https://openalex.org/I40347166"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Siyuan Xia","raw_affiliation_strings":["University of Chicago"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Chicago","institution_ids":["https://openalex.org/I40347166"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5115505096","display_name":"Daniel Alabi","orcid":null},"institutions":[{"id":"https://openalex.org/I157725225","display_name":"University of Illinois Urbana-Champaign","ror":"https://ror.org/047426m28","country_code":"US","type":"education","lineage":["https://openalex.org/I157725225"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Daniel Alabi","raw_affiliation_strings":["University of Illinois Urbana-Champaign"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Illinois Urbana-Champaign","institution_ids":["https://openalex.org/I157725225"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5049016095","display_name":"Eugene Wu","orcid":"https://orcid.org/0000-0003-4254-6688"},"institutions":[{"id":"https://openalex.org/I78577930","display_name":"Columbia University","ror":"https://ror.org/00hj8s172","country_code":"US","type":"education","lineage":["https://openalex.org/I78577930"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Eugene Wu","raw_affiliation_strings":["Columbia University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Columbia University","institution_ids":["https://openalex.org/I78577930"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"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":0.25109907,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"18","issue":"11","first_page":"4158","last_page":"4170"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11719","display_name":"Data Quality and Management","score":0.9986000061035156,"subfield":{"id":"https://openalex.org/subfields/1803","display_name":"Management Science and Operations Research"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},"topics":[{"id":"https://openalex.org/T11719","display_name":"Data Quality and Management","score":0.9986000061035156,"subfield":{"id":"https://openalex.org/subfields/1803","display_name":"Management Science and Operations Research"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T11303","display_name":"Bayesian Modeling and Causal Inference","score":0.9979000091552734,"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/T11986","display_name":"Scientific Computing and Data Management","score":0.9954000115394592,"subfield":{"id":"https://openalex.org/subfields/1802","display_name":"Information Systems and Management"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/confounding","display_name":"Confounding","score":0.5573176145553589},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5404191613197327},{"id":"https://openalex.org/keywords/scalability","display_name":"Scalability","score":0.5237522125244141},{"id":"https://openalex.org/keywords/data-science","display_name":"Data science","score":0.45211729407310486},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.38798627257347107},{"id":"https://openalex.org/keywords/database","display_name":"Database","score":0.1862606704235077},{"id":"https://openalex.org/keywords/medicine","display_name":"Medicine","score":0.16092336177825928},{"id":"https://openalex.org/keywords/internal-medicine","display_name":"Internal medicine","score":0.06570377945899963}],"concepts":[{"id":"https://openalex.org/C77350462","wikidata":"https://www.wikidata.org/wiki/Q1125472","display_name":"Confounding","level":2,"score":0.5573176145553589},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5404191613197327},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.5237522125244141},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.45211729407310486},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.38798627257347107},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.1862606704235077},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.16092336177825928},{"id":"https://openalex.org/C126322002","wikidata":"https://www.wikidata.org/wiki/Q11180","display_name":"Internal medicine","level":1,"score":0.06570377945899963}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.14778/3749646.3749684","is_oa":false,"landing_page_url":"https://doi.org/10.14778/3749646.3749684","pdf_url":null,"source":{"id":"https://openalex.org/S4210226185","display_name":"Proceedings of the VLDB Endowment","issn_l":"2150-8097","issn":["2150-8097"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319798","host_organization_name":"Association for Computing Machinery","host_organization_lineage":["https://openalex.org/P4310319798"],"host_organization_lineage_names":["Association for Computing Machinery"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the VLDB Endowment","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":25,"referenced_works":["https://openalex.org/W1971790785","https://openalex.org/W1982366717","https://openalex.org/W2132507555","https://openalex.org/W2151226328","https://openalex.org/W2293299776","https://openalex.org/W2753821949","https://openalex.org/W2796901885","https://openalex.org/W2797510035","https://openalex.org/W2798682670","https://openalex.org/W2948163032","https://openalex.org/W2983831776","https://openalex.org/W3013886221","https://openalex.org/W3021064838","https://openalex.org/W3031898476","https://openalex.org/W3042860357","https://openalex.org/W3123163299","https://openalex.org/W4286447321","https://openalex.org/W4287710841","https://openalex.org/W4289533971","https://openalex.org/W4321448337","https://openalex.org/W4366327856","https://openalex.org/W4393183674","https://openalex.org/W4399156240","https://openalex.org/W4399428786","https://openalex.org/W4399587809"],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W2899084033","https://openalex.org/W2748952813","https://openalex.org/W2994176440","https://openalex.org/W2510575233","https://openalex.org/W2481749367","https://openalex.org/W830718730","https://openalex.org/W2495367848","https://openalex.org/W2793477322","https://openalex.org/W4412082903"],"abstract_inverted_index":{"Understanding":[0],"the":[1,18,36,89,100,127,144,172,177,183,187,194],"causal":[2,71,94,130,138,152,162,189],"relationships":[3],"between":[4,146,171],"treatments":[5],"and":[6,25,132,148,176,236],"outcomes":[7],"is":[8,103,208],"fundamental":[9],"in":[10,99,126,151,186],"various":[11],"areas.":[12],"Causal":[13],"inference":[14],"aims":[15],"to":[16,30,50,59,114,157,193,200,252],"estimate":[17],"effect":[19,139],"of":[20,67,91,129,174,179,182],"one":[21],"variable":[22,185],"on":[23,28,106,233],"another,":[24],"critically":[26],"relies":[27],"access":[29,192],"those":[31],"variables":[32],"as":[33,35],"well":[34],"key":[37],"confounders.":[38,246],"Unfortunately,":[39],"data":[40,54,116,230],"analysts":[41],"often":[42],"start":[43],"with":[44,63,118],"datasets":[45,62,98,238],"lacking":[46],"these":[47,81],"columns,":[48],"leading":[49],"incorrect":[51],"estimations.":[52],"Relational":[53],"repositories":[55,117],"hold":[56],"significant":[57],"potential":[58],"augment":[60],"such":[61],"an":[64,205],"admissible":[65,206],"set":[66,207],"confounders":[68,125,160,175,203,226,266],"necessary":[69],"for":[70,78,161,224],"analysis.":[72],"While":[73],"recent":[74],"work":[75],"has":[76],"advocated":[77],"this":[79,165],"potential,":[80],"approaches":[82],"face":[83],"notable":[84],"limitations.":[85],"They":[86],"either":[87],"assume":[88],"existence":[90,173],"a":[92,137,169,213,220],"complete":[93],"diagram":[95],"over":[96],"all":[97],"repository,":[101],"which":[102],"impractical;":[104],"rely":[105],"computationally":[107],"infeasible":[108],"techniques":[109],"that":[110,143,218,240,262],"do":[111],"not":[112],"scale":[113],"large":[115,228],"many":[119],"features;":[120],"or":[121],"can":[122,154],"only":[123],"detect":[124],"absence":[128],"relations,":[131],"are":[133],"thus":[134],"ineffective":[135],"when":[136],"exists.":[140],"We":[141,210],"observe":[142],"asymmetry":[145],"causes":[147],"effects":[149],"used":[150],"discovery":[153,255,274],"be":[155],"exploited":[156],"directly":[158],"identify":[159],"queries.":[163],"In":[164],"paper,":[166],"we":[167],"establish":[168],"connection":[170],"presence":[178],"unconfounded":[180],"ancestors":[181],"treatment":[184],"underlying":[188],"diagram\u2014without":[190],"requiring":[191],"diagram.":[195],"This":[196],"makes":[197],"it":[198],"feasible":[199],"iteratively":[201],"discover":[202],"until":[204],"constructed.":[209],"propose":[211],"Suna,":[212],"highly":[214],"optimized,":[215],"GPU-compatible":[216],"system":[217,242],"implements":[219],"novel":[221],"end-to-end":[222],"algorithm":[223],"discovering":[225],"within":[227],"relational":[229],"repositories.":[231],"Experiments":[232],"both":[234],"real-world":[235],"synthetic":[237],"demonstrate":[239],"our":[241],"effectively":[243],"discovers":[244],"high-quality":[245,265],"Furthermore,":[247],"Suna":[248,263],"employs":[249],"algorithmic":[250],"optimizations":[251],"accelerate":[253],"confounder":[254,273],"without":[256],"materializing":[257],"joins.":[258],"Our":[259],"experiments":[260],"show":[261],"finds":[264],"while":[267],"running":[268],"&gt;100x":[269],"faster":[270],"than":[271],"existing":[272],"systems.":[275]},"counts_by_year":[],"updated_date":"2026-05-21T06:26:12.895304","created_date":"2025-10-10T00:00:00"}
