{"id":"https://openalex.org/W7130411008","doi":"https://doi.org/10.1145/3797881","title":"Causal Discovery by Multi-Level Wavelet Mapping Correlation Based Statistical Dependence Measurement","display_name":"Causal Discovery by Multi-Level Wavelet Mapping Correlation Based Statistical Dependence Measurement","publication_year":2026,"publication_date":"2026-02-18","ids":{"openalex":"https://openalex.org/W7130411008","doi":"https://doi.org/10.1145/3797881"},"language":"en","primary_location":{"id":"doi:10.1145/3797881","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3797881","pdf_url":null,"source":{"id":"https://openalex.org/S41523882","display_name":"ACM Transactions on Knowledge Discovery from Data","issn_l":"1556-4681","issn":["1556-4681","1556-472X"],"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":"ACM Transactions on Knowledge Discovery from Data","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/A5103395890","display_name":"Y. H. Ren","orcid":null},"institutions":[{"id":"https://openalex.org/I24943067","display_name":"Fudan University","ror":"https://ror.org/013q1eq08","country_code":"CN","type":"education","lineage":["https://openalex.org/I24943067"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yixin Ren","raw_affiliation_strings":["Shanghai Key Lab of Intelligent Information Processing, and College of Computer Science and Artificial Intelligence, Fudan University, Shanghai, China"],"raw_orcid":"https://orcid.org/0000-0002-0084-4903","affiliations":[{"raw_affiliation_string":"Shanghai Key Lab of Intelligent Information Processing, and College of Computer Science and Artificial Intelligence, Fudan University, Shanghai, China","institution_ids":["https://openalex.org/I24943067"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Hao Zhang","orcid":"https://orcid.org/0000-0001-5544-5347"},"institutions":[{"id":"https://openalex.org/I19820366","display_name":"Chinese Academy of Sciences","ror":"https://ror.org/034t30j35","country_code":"CN","type":"government","lineage":["https://openalex.org/I19820366"]},{"id":"https://openalex.org/I4210145761","display_name":"Shenzhen Institutes of Advanced Technology","ror":"https://ror.org/04gh4er46","country_code":"CN","type":"facility","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210145761"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Hao Zhang","raw_affiliation_strings":["SIAT, Chinese Academy of Sciences, Shenzhen, China"],"raw_orcid":"https://orcid.org/0000-0001-5544-5347","affiliations":[{"raw_affiliation_string":"SIAT, Chinese Academy of Sciences, Shenzhen, China","institution_ids":["https://openalex.org/I19820366","https://openalex.org/I4210145761"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5066746163","display_name":"Yewei Xia","orcid":"https://orcid.org/0000-0001-5515-5913"},"institutions":[{"id":"https://openalex.org/I24943067","display_name":"Fudan University","ror":"https://ror.org/013q1eq08","country_code":"CN","type":"education","lineage":["https://openalex.org/I24943067"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yewei Xia","raw_affiliation_strings":["Shanghai Key Lab of Intelligent Information Processing, and College of Computer Science and Artificial Intelligence, Fudan University, Shanghai, China"],"raw_orcid":"https://orcid.org/0000-0001-5515-5913","affiliations":[{"raw_affiliation_string":"Shanghai Key Lab of Intelligent Information Processing, and College of Computer Science and Artificial Intelligence, Fudan University, Shanghai, China","institution_ids":["https://openalex.org/I24943067"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5046970127","display_name":"Feng Xie","orcid":"https://orcid.org/0000-0001-7229-3955"},"institutions":[{"id":"https://openalex.org/I179026463","display_name":"Beijing Technology and Business University","ror":"https://ror.org/013e0zm98","country_code":"CN","type":"education","lineage":["https://openalex.org/I179026463"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Feng Xie","raw_affiliation_strings":["Department of Applied Statistics, Beijing Technology and Business University, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0001-7229-3955","affiliations":[{"raw_affiliation_string":"Department of Applied Statistics, Beijing Technology and Business University, Beijing, China","institution_ids":["https://openalex.org/I179026463"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5126332805","display_name":"Jihong Guan","orcid":null},"institutions":[{"id":"https://openalex.org/I116953780","display_name":"Tongji University","ror":"https://ror.org/03rc6as71","country_code":"CN","type":"education","lineage":["https://openalex.org/I116953780"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jihong Guan","raw_affiliation_strings":["Department of Computer Science and Technology, Tongji University, Shanghai, China"],"raw_orcid":"https://orcid.org/0000-0003-2313-7635","affiliations":[{"raw_affiliation_string":"Department of Computer Science and Technology, Tongji University, Shanghai, China","institution_ids":["https://openalex.org/I116953780"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5126342584","display_name":"Shuigeng Zhou","orcid":null},"institutions":[{"id":"https://openalex.org/I24943067","display_name":"Fudan University","ror":"https://ror.org/013q1eq08","country_code":"CN","type":"education","lineage":["https://openalex.org/I24943067"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shuigeng Zhou","raw_affiliation_strings":["Shanghai Key Lab of Intelligent Information Processing, and College of Computer Science and Artificial Intelligence, Fudan University, Shanghai, China"],"raw_orcid":"https://orcid.org/0000-0002-1949-2768","affiliations":[{"raw_affiliation_string":"Shanghai Key Lab of Intelligent Information Processing, and College of Computer Science and Artificial Intelligence, Fudan University, Shanghai, China","institution_ids":["https://openalex.org/I24943067"]}]}],"institutions":[],"countries_distinct_count":1,"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":0.20080687,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"20","issue":"4","first_page":"1","last_page":"33"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11303","display_name":"Bayesian Modeling and Causal Inference","score":0.982200026512146,"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/T11303","display_name":"Bayesian Modeling and Causal Inference","score":0.982200026512146,"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/T10845","display_name":"Advanced Causal Inference Techniques","score":0.005200000014156103,"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/T11063","display_name":"Rough Sets and Fuzzy Logic","score":0.0008999999845400453,"subfield":{"id":"https://openalex.org/subfields/1703","display_name":"Computational Theory and Mathematics"},"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/false-discovery-rate","display_name":"False discovery rate","score":0.7174000144004822},{"id":"https://openalex.org/keywords/independence","display_name":"Independence (probability theory)","score":0.6955000162124634},{"id":"https://openalex.org/keywords/conditional-independence","display_name":"Conditional independence","score":0.6183000206947327},{"id":"https://openalex.org/keywords/wavelet","display_name":"Wavelet","score":0.5992000102996826},{"id":"https://openalex.org/keywords/statistical-hypothesis-testing","display_name":"Statistical hypothesis testing","score":0.4999000132083893},{"id":"https://openalex.org/keywords/sample-size-determination","display_name":"Sample size determination","score":0.48069998621940613},{"id":"https://openalex.org/keywords/type-i-and-type-ii-errors","display_name":"Type I and type II errors","score":0.4458000063896179},{"id":"https://openalex.org/keywords/null-hypothesis","display_name":"Null hypothesis","score":0.42829999327659607},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.39969998598098755}],"concepts":[{"id":"https://openalex.org/C193244246","wikidata":"https://www.wikidata.org/wiki/Q5432696","display_name":"False discovery rate","level":3,"score":0.7174000144004822},{"id":"https://openalex.org/C35651441","wikidata":"https://www.wikidata.org/wiki/Q625303","display_name":"Independence (probability theory)","level":2,"score":0.6955000162124634},{"id":"https://openalex.org/C79772020","wikidata":"https://www.wikidata.org/wiki/Q5159264","display_name":"Conditional independence","level":2,"score":0.6183000206947327},{"id":"https://openalex.org/C47432892","wikidata":"https://www.wikidata.org/wiki/Q831390","display_name":"Wavelet","level":2,"score":0.5992000102996826},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.5117999911308289},{"id":"https://openalex.org/C87007009","wikidata":"https://www.wikidata.org/wiki/Q210832","display_name":"Statistical hypothesis testing","level":2,"score":0.4999000132083893},{"id":"https://openalex.org/C129848803","wikidata":"https://www.wikidata.org/wiki/Q2564360","display_name":"Sample size determination","level":2,"score":0.48069998621940613},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.46459999680519104},{"id":"https://openalex.org/C40696583","wikidata":"https://www.wikidata.org/wiki/Q989120","display_name":"Type I and type II errors","level":2,"score":0.4458000063896179},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.44350001215934753},{"id":"https://openalex.org/C191988596","wikidata":"https://www.wikidata.org/wiki/Q628374","display_name":"Null hypothesis","level":2,"score":0.42829999327659607},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.39969998598098755},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3995000123977661},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.39010000228881836},{"id":"https://openalex.org/C120567893","wikidata":"https://www.wikidata.org/wiki/Q1582085","display_name":"Knowledge extraction","level":2,"score":0.3813999891281128},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.37439998984336853},{"id":"https://openalex.org/C198531522","wikidata":"https://www.wikidata.org/wiki/Q485146","display_name":"Sample (material)","level":2,"score":0.3677000105381012},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.35920000076293945},{"id":"https://openalex.org/C34388435","wikidata":"https://www.wikidata.org/wiki/Q2267362","display_name":"Bounded function","level":2,"score":0.35350000858306885},{"id":"https://openalex.org/C2908647359","wikidata":"https://www.wikidata.org/wiki/Q2625603","display_name":"Population","level":2,"score":0.3409000039100647},{"id":"https://openalex.org/C183905921","wikidata":"https://www.wikidata.org/wiki/Q1038757","display_name":"Multiple comparisons problem","level":2,"score":0.33719998598098755},{"id":"https://openalex.org/C121694360","wikidata":"https://www.wikidata.org/wiki/Q5282862","display_name":"Distance correlation","level":3,"score":0.33320000767707825},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.3262999951839447},{"id":"https://openalex.org/C117220453","wikidata":"https://www.wikidata.org/wiki/Q5172842","display_name":"Correlation","level":2,"score":0.3246999979019165},{"id":"https://openalex.org/C2780009758","wikidata":"https://www.wikidata.org/wiki/Q6804172","display_name":"Measure (data warehouse)","level":2,"score":0.27090001106262207},{"id":"https://openalex.org/C156216132","wikidata":"https://www.wikidata.org/wiki/Q17099642","display_name":"Local independence","level":4,"score":0.2639999985694885},{"id":"https://openalex.org/C64357122","wikidata":"https://www.wikidata.org/wiki/Q1149766","display_name":"Causality (physics)","level":2,"score":0.25529998540878296},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.2551000118255615},{"id":"https://openalex.org/C87465248","wikidata":"https://www.wikidata.org/wiki/Q1417790","display_name":"Minimum description length","level":2,"score":0.25209999084472656}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3797881","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3797881","pdf_url":null,"source":{"id":"https://openalex.org/S41523882","display_name":"ACM Transactions on Knowledge Discovery from Data","issn_l":"1556-4681","issn":["1556-4681","1556-472X"],"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":"ACM Transactions on Knowledge Discovery from Data","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G3772475599","display_name":null,"funder_award_id":"62372116, 62372326, 62472415","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":22,"referenced_works":["https://openalex.org/W1565176583","https://openalex.org/W1881549354","https://openalex.org/W1975062332","https://openalex.org/W2005388202","https://openalex.org/W2006407062","https://openalex.org/W2011357767","https://openalex.org/W2018582985","https://openalex.org/W2023205960","https://openalex.org/W2034139177","https://openalex.org/W2037338351","https://openalex.org/W2048580686","https://openalex.org/W2073307618","https://openalex.org/W2073869303","https://openalex.org/W2166121267","https://openalex.org/W2536869237","https://openalex.org/W2991327923","https://openalex.org/W3003611953","https://openalex.org/W3017541653","https://openalex.org/W4255272544","https://openalex.org/W4389921502","https://openalex.org/W4390490824","https://openalex.org/W4391448991"],"related_works":[],"abstract_inverted_index":{"This":[0],"article":[1],"proposes":[2],"a":[3,11,39,66,122,134,197,245,306],"new":[4],"method":[5,137],"for":[6,274],"causal":[7,135,144,154,176,287,302],"discovery":[8,136,145,155,177,195,303],"based":[9],"on":[10,128,149],"novel":[12,246],"dependence":[13],"measurement":[14],"criterion,":[15],"namely,":[16],"Multi-level":[17],"Wavelet":[18,73],"Mapping":[19],"Correlation":[20],"(MWMC).":[21],"MWMC":[22,49],"captures":[23],"nonlinear":[24],"dependencies":[25],"between":[26],"variables":[27],"by":[28,109,138],"measuring":[29],"their":[30],"correlations":[31],"across":[32,305],"multiple":[33,301],"levels":[34],"of":[35,48,61,125,181,240,259,272,300,308],"wavelet":[36],"mappings.":[37],"From":[38],"theoretical":[40,130],"perspective,":[41],"we":[42,63,132,243],"show":[43,293],"that":[44,82,98,167,294],"the":[45,58,72,88,99,117,206,238,275,298],"empirical":[46,291],"estimate":[47],"converges":[50],"exponentially":[51],"fast":[52],"to":[53,204,212,269,283],"its":[54],"population":[55],"quantity.":[56],"Under":[57],"null":[59],"hypothesis":[60],"independence,":[62],"further":[64,280],"design":[65],"permutation-based":[67],"independence":[68,151,173,220,224,241],"testing":[69,152,174,285],"procedure,":[70],"termed":[71],"Independence":[74],"Test":[75],"(WIT),":[76],"built":[77],"upon":[78],"MWMC.":[79],"We":[80,279],"prove":[81],"WIT":[83,141,282],"not":[84],"only":[85],"effectively":[86],"controls":[87],"Type":[89,100,183,252],"I":[90],"error":[91,102,185,254],"rate":[92,103],"(false":[93,104],"positives),":[94],"but":[95],"also":[96],"guarantees":[97],"II":[101,184,253],"negatives)":[105],"is":[106,196],"upper":[107],"bounded":[108],"\\(\\mathcal{O}(n^{-1})\\)":[110],",":[111],"where":[112],"\\(":[113],"n":[114],"\\)":[115],"denotes":[116],"sample":[118,164],"size,":[119],"even":[120],"with":[121,162],"finite":[123],"number":[124],"permutations.":[126],"Building":[127],"these":[129],"guarantees,":[131],"derive":[133],"integrating":[139],"MWMC-based":[140],"into":[142],"standard":[143],"pipelines.":[146],"Extensive":[147],"experiments":[148],"(conditional)":[150],"and":[153,159,175,187,216,222,286,289],"using":[156],"both":[157],"synthetic":[158],"real-world":[160],"datasets":[161],"varying":[163],"sizes":[165],"demonstrate":[166],"our":[168],"approach":[169],"consistently":[170,296],"outperforms":[171],"existing":[172,277],"methods":[178],"in":[179,200,210,233,256],"terms":[180],"reduced":[182],"rates":[186,255],"statistically":[188],"validated":[189],"performance":[190,299],"improvements.":[191],"Impact":[192],"Statement":[193],"\u2014Causal":[194],"fundamental":[198],"task":[199],"knowledge":[201],"discovery,":[202,288],"aiming":[203],"uncover":[205],"underlying":[207],"data-generating":[208],"mechanisms":[209],"order":[211],"support":[213],"more":[214],"accurate":[215],"interpretable":[217],"predictions.":[218],"Statistical":[219],"tests":[221,226],"conditional":[223],"(CI)":[225],"have":[227],"long":[228],"served":[229],"as":[230],"core":[231],"tools":[232],"this":[234],"area.":[235],"To":[236],"improve":[237],"reliability":[239],"testing,":[242],"propose":[244],"test,":[247],"WIT,":[248],"which":[249],"achieves":[250],"lower":[251],"19":[257],"out":[258,271],"25":[260,273],"distinct":[261],"experimental":[262,309],"scenarios":[263],"involving":[264],"diverse":[265],"data":[266],"distributions,":[267],"compared":[268],"15":[270],"strongest":[276],"baseline.":[278],"apply":[281],"CI":[284],"extensive":[290],"results":[292],"it":[295],"improves":[297],"algorithms":[304],"range":[307],"settings.":[310]},"counts_by_year":[],"updated_date":"2026-07-22T07:51:19.307946","created_date":"2026-02-19T00:00:00"}
