{"id":"https://openalex.org/W2792282321","doi":"https://doi.org/10.1162/neco_a_01099","title":"Confounder Detection in High-Dimensional Linear Models Using First Moments of Spectral Measures","display_name":"Confounder Detection in High-Dimensional Linear Models Using First Moments of Spectral Measures","publication_year":2018,"publication_date":"2018-06-12","ids":{"openalex":"https://openalex.org/W2792282321","doi":"https://doi.org/10.1162/neco_a_01099","mag":"2792282321","pmid":"https://pubmed.ncbi.nlm.nih.gov/29894655"},"language":"en","primary_location":{"id":"doi:10.1162/neco_a_01099","is_oa":false,"landing_page_url":"https://doi.org/10.1162/neco_a_01099","pdf_url":null,"source":{"id":"https://openalex.org/S207023548","display_name":"Neural Computation","issn_l":"0899-7667","issn":["0899-7667","1530-888X"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310315718","host_organization_name":"The MIT Press","host_organization_lineage":["https://openalex.org/P4310315718"],"host_organization_lineage_names":["The MIT Press"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Neural Computation","raw_type":"journal-article"},"type":"article","indexed_in":["arxiv","crossref","pubmed"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/1803.06852","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Furui Liu","orcid":null},"institutions":[{"id":"https://openalex.org/I177725633","display_name":"Chinese University of Hong Kong","ror":"https://ror.org/00t33hh48","country_code":"HK","type":"education","lineage":["https://openalex.org/I177725633"]}],"countries":["HK"],"is_corresponding":false,"raw_author_name":"Furui Liu","raw_affiliation_strings":["Department of Computer Science and Engineering, Chinese University of Hong Kong, Hong Kong 999077"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Science and Engineering, Chinese University of Hong Kong, Hong Kong 999077","institution_ids":["https://openalex.org/I177725633"]}]},{"author_position":"last","author":{"id":null,"display_name":"Laiwan Chan","orcid":null},"institutions":[{"id":"https://openalex.org/I177725633","display_name":"Chinese University of Hong Kong","ror":"https://ror.org/00t33hh48","country_code":"HK","type":"education","lineage":["https://openalex.org/I177725633"]}],"countries":["HK"],"is_corresponding":false,"raw_author_name":"Laiwan Chan","raw_affiliation_strings":["Department of Computer Science and Engineering, Chinese University of Hong Kong, Hong Kong 999077"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Science and Engineering, Chinese University of Hong Kong, Hong Kong 999077","institution_ids":["https://openalex.org/I177725633"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.4825,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.6535208,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":"30","issue":"8","first_page":"2284","last_page":"2318"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10136","display_name":"Statistical Methods and Inference","score":0.23559999465942383,"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"}},"topics":[{"id":"https://openalex.org/T10136","display_name":"Statistical Methods and Inference","score":0.23559999465942383,"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/T10845","display_name":"Advanced Causal Inference Techniques","score":0.1737000048160553,"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/T11303","display_name":"Bayesian Modeling and Causal Inference","score":0.06759999692440033,"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/measure","display_name":"Measure (data warehouse)","score":0.8016999959945679},{"id":"https://openalex.org/keywords/moment","display_name":"Moment (physics)","score":0.6510000228881836},{"id":"https://openalex.org/keywords/covariance","display_name":"Covariance","score":0.6477000117301941},{"id":"https://openalex.org/keywords/covariance-matrix","display_name":"Covariance matrix","score":0.5541999936103821},{"id":"https://openalex.org/keywords/metric","display_name":"Metric (unit)","score":0.5490000247955322},{"id":"https://openalex.org/keywords/linear-regression","display_name":"Linear regression","score":0.5231999754905701},{"id":"https://openalex.org/keywords/linear-model","display_name":"Linear model","score":0.44040000438690186},{"id":"https://openalex.org/keywords/regression-analysis","display_name":"Regression analysis","score":0.39160001277923584}],"concepts":[{"id":"https://openalex.org/C2780009758","wikidata":"https://www.wikidata.org/wiki/Q6804172","display_name":"Measure (data warehouse)","level":2,"score":0.8016999959945679},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.7494999766349792},{"id":"https://openalex.org/C179254644","wikidata":"https://www.wikidata.org/wiki/Q13222844","display_name":"Moment (physics)","level":2,"score":0.6510000228881836},{"id":"https://openalex.org/C178650346","wikidata":"https://www.wikidata.org/wiki/Q201984","display_name":"Covariance","level":2,"score":0.6477000117301941},{"id":"https://openalex.org/C185142706","wikidata":"https://www.wikidata.org/wiki/Q1134404","display_name":"Covariance matrix","level":2,"score":0.5541999936103821},{"id":"https://openalex.org/C176217482","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Metric (unit)","level":2,"score":0.5490000247955322},{"id":"https://openalex.org/C48921125","wikidata":"https://www.wikidata.org/wiki/Q10861030","display_name":"Linear regression","level":2,"score":0.5231999754905701},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.4641000032424927},{"id":"https://openalex.org/C163175372","wikidata":"https://www.wikidata.org/wiki/Q3339222","display_name":"Linear model","level":2,"score":0.44040000438690186},{"id":"https://openalex.org/C28826006","wikidata":"https://www.wikidata.org/wiki/Q33521","display_name":"Applied mathematics","level":1,"score":0.3937000036239624},{"id":"https://openalex.org/C152877465","wikidata":"https://www.wikidata.org/wiki/Q208042","display_name":"Regression analysis","level":2,"score":0.39160001277923584},{"id":"https://openalex.org/C57691317","wikidata":"https://www.wikidata.org/wiki/Q1289248","display_name":"Scalar (mathematics)","level":2,"score":0.3855000138282776},{"id":"https://openalex.org/C106487976","wikidata":"https://www.wikidata.org/wiki/Q685816","display_name":"Matrix (chemical analysis)","level":2,"score":0.37619999051094055},{"id":"https://openalex.org/C77350462","wikidata":"https://www.wikidata.org/wiki/Q1125472","display_name":"Confounding","level":2,"score":0.36469998955726624},{"id":"https://openalex.org/C203233044","wikidata":"https://www.wikidata.org/wiki/Q5264358","display_name":"Design matrix","level":3,"score":0.349700003862381},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.3375000059604645},{"id":"https://openalex.org/C2777634741","wikidata":"https://www.wikidata.org/wiki/Q768993","display_name":"Wasserstein metric","level":2,"score":0.30079999566078186},{"id":"https://openalex.org/C38976095","wikidata":"https://www.wikidata.org/wiki/Q752641","display_name":"Asymmetry","level":2,"score":0.2985999882221222},{"id":"https://openalex.org/C152822103","wikidata":"https://www.wikidata.org/wiki/Q7575207","display_name":"Spectral shape analysis","level":3,"score":0.2930000126361847},{"id":"https://openalex.org/C114289077","wikidata":"https://www.wikidata.org/wiki/Q3284399","display_name":"Statistical model","level":2,"score":0.2705000042915344},{"id":"https://openalex.org/C2983726731","wikidata":"https://www.wikidata.org/wiki/Q7575215","display_name":"Spectral measure","level":2,"score":0.2678999900817871},{"id":"https://openalex.org/C131109320","wikidata":"https://www.wikidata.org/wiki/Q581012","display_name":"Linear prediction","level":2,"score":0.26190000772476196},{"id":"https://openalex.org/C182365436","wikidata":"https://www.wikidata.org/wiki/Q50701","display_name":"Variable (mathematics)","level":2,"score":0.25929999351501465},{"id":"https://openalex.org/C121864883","wikidata":"https://www.wikidata.org/wiki/Q677916","display_name":"Statistical physics","level":1,"score":0.2549000084400177}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1162/neco_a_01099","is_oa":false,"landing_page_url":"https://doi.org/10.1162/neco_a_01099","pdf_url":null,"source":{"id":"https://openalex.org/S207023548","display_name":"Neural Computation","issn_l":"0899-7667","issn":["0899-7667","1530-888X"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310315718","host_organization_name":"The MIT Press","host_organization_lineage":["https://openalex.org/P4310315718"],"host_organization_lineage_names":["The MIT Press"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Neural Computation","raw_type":"journal-article"},{"id":"pmid:29894655","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/29894655","pdf_url":null,"source":{"id":"https://openalex.org/S4306525036","display_name":"PubMed","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Neural computation","raw_type":null},{"id":"pmh:oai:arXiv.org:1803.06852","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1803.06852","pdf_url":"https://arxiv.org/pdf/1803.06852","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":"pmh:oai:arXiv.org:1803.06852","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1803.06852","pdf_url":"https://arxiv.org/pdf/1803.06852","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"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":10,"referenced_works":["https://openalex.org/W1973286131","https://openalex.org/W1998123258","https://openalex.org/W2010464263","https://openalex.org/W2051094226","https://openalex.org/W2083689856","https://openalex.org/W2093947494","https://openalex.org/W2094810855","https://openalex.org/W2271150192","https://openalex.org/W2284008599","https://openalex.org/W2606946190"],"related_works":[],"abstract_inverted_index":{"In":[0,101],"this":[1,102,211],"letter,":[2,103],"we":[3,104],"study":[4,45],"the":[5,10,14,42,48,53,60,86,108,112,120,124,133,146,166,181,185,192,208],"confounder":[6,116,178],"detection":[7],"problem":[8],"in":[9,73,85,157,165],"linear":[11],"model,":[12,43],"where":[13],"target":[15],"variable":[16],"[Formula:":[17,24,29,64,150],"see":[18,25,30,65,151],"text]":[19,26,66],"is":[20,67],"predicted":[21],"using":[22],"its":[23],"potential":[27],"causes":[28],"text].":[31,152],"Based":[32],"on":[33,202],"an":[34],"assumption":[35],"of":[36,41,63,88,111,123,136,149,168,194,210],"a":[37,70,81,89,137],"rotation-invariant":[38],"generating":[39],"process":[40],"recent":[44],"shows":[46],"that":[47],"spectral":[49,93,113,127,139,186],"measure":[50,72,83,94,114,128,187],"induced":[51],"by":[52],"regression":[54,125],"coefficient":[55],"vector":[56],"with":[57,132,143],"respect":[58,144],"to":[59,69,98,106,145,183],"covariance":[61,147],"matrix":[62,148],"close":[68],"uniform":[71,82,138],"purely":[74],"causal":[75],"cases,":[76],"but":[77],"it":[78,131],"differs":[79],"from":[80,162],"characteristically":[84],"presence":[87,167],"scalar":[90],"confounder.":[91],"Analyzing":[92],"patterns":[95],"could":[96],"help":[97],"detect":[99],"confounding.":[100,169],"propose":[105],"use":[107],"first":[109,121,134],"moment":[110,122,135],"for":[115,177],"detection.":[117,179],"We":[118],"calculate":[119],"vector-induced":[126],"and":[129,160,197,204],"compare":[130],"measure,":[140],"both":[141],"defined":[142],"The":[153],"two":[154],"moments":[155],"coincide":[156],"nonconfounding":[158],"cases":[159],"differ":[161],"each":[163],"other":[164],"This":[170],"statistical":[171],"causal-confounding":[172],"asymmetry":[173],"can":[174],"be":[175],"used":[176],"Without":[180],"need":[182],"analyze":[184],"pattern,":[188],"our":[189],"method":[190],"avoids":[191],"difficulty":[193],"metric":[195],"choice":[196],"multiple":[198],"parameter":[199],"optimization.":[200],"Experiments":[201],"synthetic":[203],"real":[205],"data":[206],"show":[207],"performance":[209],"method.":[212]},"counts_by_year":[{"year":2023,"cited_by_count":1},{"year":2021,"cited_by_count":1},{"year":2019,"cited_by_count":1}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2018-03-29T00:00:00"}
