{"id":"https://openalex.org/W2972323282","doi":"https://doi.org/10.1145/3313147","title":"Local Learning Approaches for Finding Effects of a Specified Cause and Their Causal Paths","display_name":"Local Learning Approaches for Finding Effects of a Specified Cause and Their Causal Paths","publication_year":2019,"publication_date":"2019-09-12","ids":{"openalex":"https://openalex.org/W2972323282","doi":"https://doi.org/10.1145/3313147","mag":"2972323282"},"language":"en","primary_location":{"id":"doi:10.1145/3313147","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3313147","pdf_url":null,"source":{"id":"https://openalex.org/S2492086750","display_name":"ACM Transactions on Intelligent Systems and Technology","issn_l":"2157-6904","issn":["2157-6904","2157-6912"],"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 Intelligent Systems and Technology","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/A5100320078","display_name":"Yue Liu","orcid":"https://orcid.org/0000-0002-5965-0644"},"institutions":[{"id":"https://openalex.org/I20231570","display_name":"Peking University","ror":"https://ror.org/02v51f717","country_code":"CN","type":"education","lineage":["https://openalex.org/I20231570"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Yue Liu","raw_affiliation_strings":["Peking University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Peking University, Beijing, China","institution_ids":["https://openalex.org/I20231570"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5080540250","display_name":"Zheng Cai","orcid":"https://orcid.org/0000-0002-9564-476X"},"institutions":[{"id":"https://openalex.org/I2250653659","display_name":"Tencent (China)","ror":"https://ror.org/00hhjss72","country_code":"CN","type":"company","lineage":["https://openalex.org/I2250653659"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zheng Cai","raw_affiliation_strings":["Tencent Technology (Shenzhen) Co. Ltd, Shenzhen, China"],"affiliations":[{"raw_affiliation_string":"Tencent Technology (Shenzhen) Co. Ltd, Shenzhen, China","institution_ids":["https://openalex.org/I2250653659"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5089535748","display_name":"Chunchen Liu","orcid":"https://orcid.org/0000-0002-5830-1805"},"institutions":[{"id":"https://openalex.org/I4210149379","display_name":"NEC (China)","ror":"https://ror.org/04xrcx824","country_code":"CN","type":"company","lineage":["https://openalex.org/I118347220","https://openalex.org/I4210149379"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chunchen Liu","raw_affiliation_strings":["NEC Laboratories, Beijing, China"],"affiliations":[{"raw_affiliation_string":"NEC Laboratories, Beijing, China","institution_ids":["https://openalex.org/I4210149379"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5036930901","display_name":"Zhi Geng","orcid":"https://orcid.org/0000-0003-1798-4475"},"institutions":[{"id":"https://openalex.org/I20231570","display_name":"Peking University","ror":"https://ror.org/02v51f717","country_code":"CN","type":"education","lineage":["https://openalex.org/I20231570"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhi Geng","raw_affiliation_strings":["Peking University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Peking University, Beijing, China","institution_ids":["https://openalex.org/I20231570"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5100320078"],"corresponding_institution_ids":["https://openalex.org/I20231570"],"apc_list":null,"apc_paid":null,"fwci":0.28,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.65761779,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":"10","issue":"5","first_page":"1","last_page":"15"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11303","display_name":"Bayesian Modeling and Causal Inference","score":0.9991999864578247,"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":0.9991999864578247,"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/T11273","display_name":"Advanced Graph Neural Networks","score":0.9695000052452087,"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/T11063","display_name":"Rough Sets and Fuzzy Logic","score":0.9574000239372253,"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/causal-model","display_name":"Causal model","score":0.7584960460662842},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7377901673316956},{"id":"https://openalex.org/keywords/identifiability","display_name":"Identifiability","score":0.7100712060928345},{"id":"https://openalex.org/keywords/variable","display_name":"Variable (mathematics)","score":0.6836614608764648},{"id":"https://openalex.org/keywords/causal-inference","display_name":"Causal inference","score":0.5629568099975586},{"id":"https://openalex.org/keywords/causality","display_name":"Causality (physics)","score":0.546619713306427},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.5373885035514832},{"id":"https://openalex.org/keywords/path","display_name":"Path (computing)","score":0.5170167684555054},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4491538405418396},{"id":"https://openalex.org/keywords/instrumental-variable","display_name":"Instrumental variable","score":0.43699952960014343},{"id":"https://openalex.org/keywords/path-analysis","display_name":"Path analysis (statistics)","score":0.4358079731464386},{"id":"https://openalex.org/keywords/causal-analysis","display_name":"Causal analysis","score":0.4331647753715515},{"id":"https://openalex.org/keywords/causal-structure","display_name":"Causal structure","score":0.4199215769767761},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3551856279373169},{"id":"https://openalex.org/keywords/econometrics","display_name":"Econometrics","score":0.2576253414154053},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.1511937975883484},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.08247038722038269}],"concepts":[{"id":"https://openalex.org/C11671645","wikidata":"https://www.wikidata.org/wiki/Q5054567","display_name":"Causal model","level":2,"score":0.7584960460662842},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7377901673316956},{"id":"https://openalex.org/C122770356","wikidata":"https://www.wikidata.org/wiki/Q1656753","display_name":"Identifiability","level":2,"score":0.7100712060928345},{"id":"https://openalex.org/C182365436","wikidata":"https://www.wikidata.org/wiki/Q50701","display_name":"Variable (mathematics)","level":2,"score":0.6836614608764648},{"id":"https://openalex.org/C158600405","wikidata":"https://www.wikidata.org/wiki/Q5054566","display_name":"Causal inference","level":2,"score":0.5629568099975586},{"id":"https://openalex.org/C64357122","wikidata":"https://www.wikidata.org/wiki/Q1149766","display_name":"Causality (physics)","level":2,"score":0.546619713306427},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.5373885035514832},{"id":"https://openalex.org/C2777735758","wikidata":"https://www.wikidata.org/wiki/Q817765","display_name":"Path (computing)","level":2,"score":0.5170167684555054},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4491538405418396},{"id":"https://openalex.org/C162144332","wikidata":"https://www.wikidata.org/wiki/Q1665305","display_name":"Instrumental variable","level":2,"score":0.43699952960014343},{"id":"https://openalex.org/C82793941","wikidata":"https://www.wikidata.org/wiki/Q1046024","display_name":"Path analysis (statistics)","level":2,"score":0.4358079731464386},{"id":"https://openalex.org/C2987525970","wikidata":"https://www.wikidata.org/wiki/Q96374569","display_name":"Causal analysis","level":2,"score":0.4331647753715515},{"id":"https://openalex.org/C163504300","wikidata":"https://www.wikidata.org/wiki/Q2364925","display_name":"Causal structure","level":2,"score":0.4199215769767761},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3551856279373169},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.2576253414154053},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.1511937975883484},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.08247038722038269},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"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/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3313147","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3313147","pdf_url":null,"source":{"id":"https://openalex.org/S2492086750","display_name":"ACM Transactions on Intelligent Systems and Technology","issn_l":"2157-6904","issn":["2157-6904","2157-6912"],"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 Intelligent Systems and Technology","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G8315720123","display_name":null,"funder_award_id":"11331011, 11771028, 91630314","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":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":16,"referenced_works":["https://openalex.org/W1501375624","https://openalex.org/W1586003574","https://openalex.org/W1975062332","https://openalex.org/W2014887862","https://openalex.org/W2031779765","https://openalex.org/W2050502901","https://openalex.org/W2101162551","https://openalex.org/W2111061246","https://openalex.org/W2130095137","https://openalex.org/W2165190832","https://openalex.org/W2897855238","https://openalex.org/W2964129930","https://openalex.org/W3104000671","https://openalex.org/W3133236490","https://openalex.org/W4285719527","https://openalex.org/W4299515571"],"related_works":["https://openalex.org/W2161504683","https://openalex.org/W4403292511","https://openalex.org/W4313422683","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/W2011959240"],"abstract_inverted_index":{"Causal":[0],"networks":[1],"are":[2,42,52,125],"used":[3],"to":[4,7,60,95,131],"describe":[5],"and":[6,13,50,87,108,115],"discover":[8],"causal":[9,26,54,68,89,110,123,139],"relationships":[10],"among":[11],"variables":[12],"data":[14,114],"generating":[15],"mechanisms.":[16],"There":[17],"have":[18],"been":[19],"many":[20,33],"approaches":[21,75,129],"for":[22,76],"learning":[23,65,74],"a":[24,46,66,133],"global":[25,67],"network":[27],"of":[28,45,64,82,99,105,141],"all":[29,78],"observed":[30,113],"variables.":[31],"In":[32],"applications,":[34],"we":[35,70],"may":[36],"be":[37],"interested":[38],"in":[39],"finding":[40,77],"what":[41,51],"the":[43,53,57,83,88,92,103,106,109,119,122,138],"effects":[44,79,107],"specified":[47,84],"cause":[48,58,85,93],"variable":[49,59,86,94,98],"paths":[55,90,111,124,140],"from":[56,91,112],"its":[61],"effects.":[62],"Instead":[63],"network,":[69],"propose":[71],"several":[72],"local":[73],"(or":[80],"descendants)":[81],"some":[96],"effect":[97],"interest.":[100,142],"We":[101],"discuss":[102],"identifiability":[104],"prior":[116],"knowledge.":[117],"For":[118],"case":[120],"that":[121,136],"not":[126],"identifiable,":[127],"our":[128],"try":[130],"find":[132],"path":[134],"set":[135],"contains":[137]},"counts_by_year":[{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
