{"id":"https://openalex.org/W2793644834","doi":"https://doi.org/10.1162/neco_a_01064","title":"A Kernel Embedding\u2013Based Approach for Nonstationary Causal Model Inference","display_name":"A Kernel Embedding\u2013Based Approach for Nonstationary Causal Model Inference","publication_year":2018,"publication_date":"2018-01-30","ids":{"openalex":"https://openalex.org/W2793644834","doi":"https://doi.org/10.1162/neco_a_01064","mag":"2793644834","pmid":"https://pubmed.ncbi.nlm.nih.gov/29381444"},"language":"en","primary_location":{"id":"doi:10.1162/neco_a_01064","is_oa":false,"landing_page_url":"https://doi.org/10.1162/neco_a_01064","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/1809.08560","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5031217579","display_name":"Shoubo Hu","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":true,"raw_author_name":"Shoubo Hu","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":"middle","author":{"id":"https://openalex.org/A5059324399","display_name":"Zhitang Chen","orcid":"https://orcid.org/0000-0001-7197-4601"},"institutions":[{"id":"https://openalex.org/I2250955327","display_name":"Huawei Technologies (China)","ror":"https://ror.org/00cmhce21","country_code":"CN","type":"company","lineage":["https://openalex.org/I2250955327"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Zhitang Chen","raw_affiliation_strings":["Noah's Ark Lab, Huawei, Hong Kong Science Park, Hong Kong 999077"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Noah's Ark Lab, Huawei, Hong Kong Science Park, Hong Kong 999077","institution_ids":["https://openalex.org/I2250955327"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5111431165","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":true,"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":2,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5031217579","https://openalex.org/A5059324399","https://openalex.org/A5111431165"],"corresponding_institution_ids":["https://openalex.org/I177725633","https://openalex.org/I2250955327"],"apc_list":null,"apc_paid":null,"fwci":0.3384,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.67133474,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":95},"biblio":{"volume":"30","issue":"5","first_page":"1394","last_page":"1425"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11303","display_name":"Bayesian Modeling and Causal Inference","score":0.9997000098228455,"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":0.9997000098228455,"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/T11719","display_name":"Data Quality and Management","score":0.9817000031471252,"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/T11447","display_name":"Blind Source Separation Techniques","score":0.9703999757766724,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"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/causal-inference","display_name":"Causal inference","score":0.7020435333251953},{"id":"https://openalex.org/keywords/kernel","display_name":"Kernel (algebra)","score":0.625137984752655},{"id":"https://openalex.org/keywords/causal-model","display_name":"Causal model","score":0.6093434691429138},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.5173079967498779},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.5083989500999451},{"id":"https://openalex.org/keywords/embedding","display_name":"Embedding","score":0.4638635516166687},{"id":"https://openalex.org/keywords/causal-structure","display_name":"Causal structure","score":0.45191675424575806},{"id":"https://openalex.org/keywords/causality","display_name":"Causality (physics)","score":0.4381347894668579},{"id":"https://openalex.org/keywords/kernel-method","display_name":"Kernel method","score":0.41859084367752075},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4035719037055969},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.3909696340560913},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.3628426492214203},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.32929527759552},{"id":"https://openalex.org/keywords/econometrics","display_name":"Econometrics","score":0.2863319516181946},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.14859098196029663},{"id":"https://openalex.org/keywords/discrete-mathematics","display_name":"Discrete mathematics","score":0.11869147419929504},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.07161545753479004}],"concepts":[{"id":"https://openalex.org/C158600405","wikidata":"https://www.wikidata.org/wiki/Q5054566","display_name":"Causal inference","level":2,"score":0.7020435333251953},{"id":"https://openalex.org/C74193536","wikidata":"https://www.wikidata.org/wiki/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.625137984752655},{"id":"https://openalex.org/C11671645","wikidata":"https://www.wikidata.org/wiki/Q5054567","display_name":"Causal model","level":2,"score":0.6093434691429138},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.5173079967498779},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.5083989500999451},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.4638635516166687},{"id":"https://openalex.org/C163504300","wikidata":"https://www.wikidata.org/wiki/Q2364925","display_name":"Causal structure","level":2,"score":0.45191675424575806},{"id":"https://openalex.org/C64357122","wikidata":"https://www.wikidata.org/wiki/Q1149766","display_name":"Causality (physics)","level":2,"score":0.4381347894668579},{"id":"https://openalex.org/C122280245","wikidata":"https://www.wikidata.org/wiki/Q620622","display_name":"Kernel method","level":3,"score":0.41859084367752075},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4035719037055969},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.3909696340560913},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3628426492214203},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.32929527759552},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.2863319516181946},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.14859098196029663},{"id":"https://openalex.org/C118615104","wikidata":"https://www.wikidata.org/wiki/Q121416","display_name":"Discrete mathematics","level":1,"score":0.11869147419929504},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.07161545753479004},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1162/neco_a_01064","is_oa":false,"landing_page_url":"https://doi.org/10.1162/neco_a_01064","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:29381444","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/29381444","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:1809.08560","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1809.08560","pdf_url":"https://arxiv.org/pdf/1809.08560","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:1809.08560","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1809.08560","pdf_url":"https://arxiv.org/pdf/1809.08560","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":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":30,"referenced_works":["https://openalex.org/W188913015","https://openalex.org/W1603640438","https://openalex.org/W1946137962","https://openalex.org/W1975062332","https://openalex.org/W1983690667","https://openalex.org/W2011680222","https://openalex.org/W2059055991","https://openalex.org/W2064052856","https://openalex.org/W2073459066","https://openalex.org/W2082293106","https://openalex.org/W2099741732","https://openalex.org/W2105766378","https://openalex.org/W2112552549","https://openalex.org/W2123649031","https://openalex.org/W2132507555","https://openalex.org/W2146531590","https://openalex.org/W2146641075","https://openalex.org/W2147881172","https://openalex.org/W2151226328","https://openalex.org/W2165582599","https://openalex.org/W2463168724","https://openalex.org/W2597289420","https://openalex.org/W2610053348","https://openalex.org/W2913754224","https://openalex.org/W2990138404","https://openalex.org/W3021064838","https://openalex.org/W3145543370","https://openalex.org/W4205575619","https://openalex.org/W4299679994","https://openalex.org/W4302423442"],"related_works":["https://openalex.org/W2161504683","https://openalex.org/W2574301230","https://openalex.org/W4386620154","https://openalex.org/W4389961576","https://openalex.org/W4294555408","https://openalex.org/W4372260129","https://openalex.org/W1153243621","https://openalex.org/W4299782962","https://openalex.org/W4386839885","https://openalex.org/W3036459125"],"abstract_inverted_index":{"Although":[0],"nonstationary":[1,32],"data":[2,37,150],"are":[3,38,82,140,151],"more":[4],"common":[5],"in":[6,75],"the":[7,50,68,72,86,91,95,112,127,155],"real":[8],"world,":[9],"most":[10],"existing":[11,161],"causal":[12,33,87,92,104,138],"discovery":[13,106],"methods":[14],"do":[15],"not":[16],"take":[17],"nonstationarity":[18,128],"into":[19,57,116],"consideration.":[20],"In":[21,46,78],"this":[22,79],"letter,":[23],"we":[24,48,81,100],"propose":[25],"a":[26,54,58,117],"kernel":[27,69],"embedding-based":[28],"approach,":[29],"ENCI,":[30,47],"for":[31,107],"model":[34,60],"inference":[35],"where":[36],"collected":[39],"from":[40],"multiple":[41,108],"domains":[42],"with":[43],"varying":[44],"distributions.":[45],"transform":[49],"complicated":[51],"relation":[52],"of":[53,61,63,71,94,129,137,157],"cause-effect":[55,132],"pair":[56],"linear":[59,97,118],"variables":[62,109],"which":[64],"observations":[65],"correspond":[66],"to":[67,84,103,153],"embeddings":[70],"cause-and-effect":[73],"distributions":[74],"different":[76],"domains.":[77],"way,":[80],"able":[83],"estimate":[85],"direction":[88],"by":[89,110,125],"exploiting":[90,126],"asymmetry":[93],"transformed":[96],"model.":[98,121],"Furthermore,":[99],"extend":[101],"ENCI":[102,158],"graph":[105],"transforming":[111],"relations":[113],"among":[114],"them":[115],"nongaussian":[119],"acyclic":[120],"We":[122],"show":[123],"that":[124],"distributions,":[130],"both":[131],"pairs":[133],"and":[134,148],"two":[135],"kinds":[136],"graphs":[139],"identifiable":[141],"under":[142],"mild":[143],"conditions.":[144],"Experiments":[145],"on":[146],"synthetic":[147],"real-world":[149],"conducted":[152],"justify":[154],"efficacy":[156],"over":[159],"major":[160],"methods.":[162]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2021,"cited_by_count":1},{"year":2019,"cited_by_count":1}],"updated_date":"2026-05-21T06:26:12.895304","created_date":"2025-10-10T00:00:00"}
