{"id":"https://openalex.org/W2023466463","doi":"https://doi.org/10.1080/01969720802039594","title":"IDENTIFIABILITY IN CAUSAL BAYESIAN NETWORKS: A GENTLE INTRODUCTION","display_name":"IDENTIFIABILITY IN CAUSAL BAYESIAN NETWORKS: A GENTLE INTRODUCTION","publication_year":2008,"publication_date":"2008-05-12","ids":{"openalex":"https://openalex.org/W2023466463","doi":"https://doi.org/10.1080/01969720802039594","mag":"2023466463"},"language":"en","primary_location":{"id":"doi:10.1080/01969720802039594","is_oa":false,"landing_page_url":"https://doi.org/10.1080/01969720802039594","pdf_url":null,"source":{"id":"https://openalex.org/S117436046","display_name":"Cybernetics & Systems","issn_l":"0196-9722","issn":["0196-9722","1087-6553"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320547","host_organization_name":"Taylor & Francis","host_organization_lineage":["https://openalex.org/P4310320547"],"host_organization_lineage_names":["Taylor & Francis"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Cybernetics and Systems","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/A5007794440","display_name":"Marco Valtorta","orcid":"https://orcid.org/0000-0001-9564-9109"},"institutions":[{"id":"https://openalex.org/I155781252","display_name":"University of South Carolina","ror":"https://ror.org/02b6qw903","country_code":"US","type":"education","lineage":["https://openalex.org/I155781252"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Marco Valtorta","raw_affiliation_strings":["Department of Computer Science and Engineering, University of South Carolina","Dept. of Computer Science and Engineering University of South Carolina#TAB#"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Science and Engineering, University of South Carolina","institution_ids":["https://openalex.org/I155781252"]},{"raw_affiliation_string":"Dept. of Computer Science and Engineering University of South Carolina#TAB#","institution_ids":["https://openalex.org/I155781252"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5020782081","display_name":"Yimin Huang","orcid":"https://orcid.org/0000-0003-4348-5688"},"institutions":[{"id":"https://openalex.org/I155781252","display_name":"University of South Carolina","ror":"https://ror.org/02b6qw903","country_code":"US","type":"education","lineage":["https://openalex.org/I155781252"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yimin Huang","raw_affiliation_strings":["Department of Computer Science and Engineering, University of South Carolina","Dept. of Computer Science and Engineering University of South Carolina#TAB#"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Science and Engineering, University of South Carolina","institution_ids":["https://openalex.org/I155781252"]},{"raw_affiliation_string":"Dept. of Computer Science and Engineering University of South Carolina#TAB#","institution_ids":["https://openalex.org/I155781252"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5007794440"],"corresponding_institution_ids":["https://openalex.org/I155781252"],"apc_list":null,"apc_paid":null,"fwci":2.0161,"has_fulltext":false,"cited_by_count":5,"citation_normalized_percentile":{"value":0.89688563,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":"39","issue":"4","first_page":"425","last_page":"442"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11303","display_name":"Bayesian Modeling and Causal Inference","score":0.9998999834060669,"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":0.9998999834060669,"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/T12072","display_name":"Machine Learning and Algorithms","score":0.9187999963760376,"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/bayesian-network","display_name":"Bayesian network","score":0.6873660683631897},{"id":"https://openalex.org/keywords/identifiability","display_name":"Identifiability","score":0.6773126721382141},{"id":"https://openalex.org/keywords/unobservable","display_name":"Unobservable","score":0.6684958934783936},{"id":"https://openalex.org/keywords/graphical-model","display_name":"Graphical model","score":0.6614553332328796},{"id":"https://openalex.org/keywords/causal-structure","display_name":"Causal structure","score":0.6611684560775757},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6466459035873413},{"id":"https://openalex.org/keywords/directed-acyclic-graph","display_name":"Directed acyclic graph","score":0.6269471645355225},{"id":"https://openalex.org/keywords/causal-model","display_name":"Causal model","score":0.6171203851699829},{"id":"https://openalex.org/keywords/causal-inference","display_name":"Causal inference","score":0.609098494052887},{"id":"https://openalex.org/keywords/joint-probability-distribution","display_name":"Joint probability distribution","score":0.5242136716842651},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.49057596921920776},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.47614455223083496},{"id":"https://openalex.org/keywords/causality","display_name":"Causality (physics)","score":0.4637589454650879},{"id":"https://openalex.org/keywords/probabilistic-logic","display_name":"Probabilistic logic","score":0.454730361700058},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4503187835216522},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.4211590886116028},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.415277898311615},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.36986613273620605},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.25215092301368713},{"id":"https://openalex.org/keywords/econometrics","display_name":"Econometrics","score":0.2289087474346161},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.20730060338974}],"concepts":[{"id":"https://openalex.org/C33724603","wikidata":"https://www.wikidata.org/wiki/Q812540","display_name":"Bayesian network","level":2,"score":0.6873660683631897},{"id":"https://openalex.org/C122770356","wikidata":"https://www.wikidata.org/wiki/Q1656753","display_name":"Identifiability","level":2,"score":0.6773126721382141},{"id":"https://openalex.org/C2780695315","wikidata":"https://www.wikidata.org/wiki/Q3799040","display_name":"Unobservable","level":2,"score":0.6684958934783936},{"id":"https://openalex.org/C155846161","wikidata":"https://www.wikidata.org/wiki/Q1143367","display_name":"Graphical model","level":2,"score":0.6614553332328796},{"id":"https://openalex.org/C163504300","wikidata":"https://www.wikidata.org/wiki/Q2364925","display_name":"Causal structure","level":2,"score":0.6611684560775757},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6466459035873413},{"id":"https://openalex.org/C74197172","wikidata":"https://www.wikidata.org/wiki/Q1195339","display_name":"Directed acyclic graph","level":2,"score":0.6269471645355225},{"id":"https://openalex.org/C11671645","wikidata":"https://www.wikidata.org/wiki/Q5054567","display_name":"Causal model","level":2,"score":0.6171203851699829},{"id":"https://openalex.org/C158600405","wikidata":"https://www.wikidata.org/wiki/Q5054566","display_name":"Causal inference","level":2,"score":0.609098494052887},{"id":"https://openalex.org/C18653775","wikidata":"https://www.wikidata.org/wiki/Q1333358","display_name":"Joint probability distribution","level":2,"score":0.5242136716842651},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.49057596921920776},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.47614455223083496},{"id":"https://openalex.org/C64357122","wikidata":"https://www.wikidata.org/wiki/Q1149766","display_name":"Causality (physics)","level":2,"score":0.4637589454650879},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.454730361700058},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4503187835216522},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.4211590886116028},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.415277898311615},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.36986613273620605},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.25215092301368713},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.2289087474346161},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.20730060338974},{"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},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1080/01969720802039594","is_oa":false,"landing_page_url":"https://doi.org/10.1080/01969720802039594","pdf_url":null,"source":{"id":"https://openalex.org/S117436046","display_name":"Cybernetics & Systems","issn_l":"0196-9722","issn":["0196-9722","1087-6553"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320547","host_organization_name":"Taylor & Francis","host_organization_lineage":["https://openalex.org/P4310320547"],"host_organization_lineage_names":["Taylor & Francis"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Cybernetics and Systems","raw_type":"journal-article"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.158.715","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.158.715","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://www.cse.sc.edu/~mgv/papers/ValtortaHuangIntroProofs.pdf","raw_type":"text"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":37,"referenced_works":["https://openalex.org/W102511605","https://openalex.org/W134202759","https://openalex.org/W275687840","https://openalex.org/W1486066286","https://openalex.org/W1524326598","https://openalex.org/W1585303438","https://openalex.org/W1591031080","https://openalex.org/W1592297672","https://openalex.org/W1673419196","https://openalex.org/W1752500127","https://openalex.org/W1755360231","https://openalex.org/W1800852440","https://openalex.org/W1953155912","https://openalex.org/W1980452149","https://openalex.org/W1989020770","https://openalex.org/W1992816730","https://openalex.org/W1994853484","https://openalex.org/W2008312103","https://openalex.org/W2008995782","https://openalex.org/W2034631460","https://openalex.org/W2049910836","https://openalex.org/W2082020799","https://openalex.org/W2143891888","https://openalex.org/W2144351155","https://openalex.org/W2157529132","https://openalex.org/W2159080219","https://openalex.org/W2522321215","https://openalex.org/W2964052841","https://openalex.org/W3133236490","https://openalex.org/W4299515571","https://openalex.org/W4299670631","https://openalex.org/W4300111185","https://openalex.org/W4388323202","https://openalex.org/W6635196568","https://openalex.org/W6636922523","https://openalex.org/W6638284894","https://openalex.org/W6648485944"],"related_works":["https://openalex.org/W2561451072","https://openalex.org/W2225091985","https://openalex.org/W2780950403","https://openalex.org/W1937963769","https://openalex.org/W2953048674","https://openalex.org/W4379365605","https://openalex.org/W2179859124","https://openalex.org/W1965943380","https://openalex.org/W1585678139","https://openalex.org/W2550397644"],"abstract_inverted_index":{"In":[0,117],"this":[1,80,122,166],"article":[2],"we":[3],"describe":[4,70],"an":[5],"important":[6],"structure":[7,196],"used":[8],"to":[9,20,152,175,182,199],"model":[10],"causal":[11,24,38,42,98,106,131],"theories":[12],"and":[13,44,84,91,133,171,185,201,212,217],"a":[14,28,32,37,45,124,128,138,148,157,180],"related":[15,214],"problem":[16,103,184,206],"of":[17,31,47,59,74,104,121,130,145,150,156,162,187,207],"great":[18],"interest":[19],"semi-empirical":[21,125],"scientists.":[22],"A":[23,94],"Bayesian":[25,99,194],"network":[26,195],"is":[27,101,112,197],"pair":[29],"consisting":[30],"directed":[33],"acyclic":[34],"graph":[35,54],"(called":[36],"graph)":[39],"that":[40,50,178,192],"represents":[41],"relationships":[43],"set":[46,129,144],"probability":[48,58,139],"tables,":[49],"together":[51,136],"with":[52,137],"the":[53,56,60,66,71,102,114,118,142,154,173,183,193,205,213],"specify":[55],"joint":[57],"variables":[61,87,146],"represented":[62],"as":[63],"nodes":[64],"in":[65,147],"graph.":[67],"We":[68,164,190],"briefly":[69],"probabilistic":[72,82],"semantics":[73],"causality":[75],"proposed":[76],"by":[77],"Pearl":[78],"for":[79],"graphical":[81],"model,":[83],"how":[85],"unobservable":[86],"greatly":[88],"complicate":[89],"models":[90],"their":[92],"application.":[93],"common":[95],"question":[96],"about":[97],"networks":[100],"identifying":[105],"effects":[107],"from":[108,210],"nonexperimental":[109],"data,":[110],"which":[111],"called":[113],"identifability":[115],"problem.":[116],"basic":[119],"version":[120],"problem,":[123,167],"scientist":[126],"postulates":[127],"mechanisms":[132],"uses":[134],"them,":[135],"distribution":[140],"on":[141,159],"observable":[143],"domain":[149],"interest,":[151],"predict":[153],"effect":[155],"manipulation":[158],"some":[160,186],"variable":[161],"interest.":[163],"explain":[165],"provide":[168],"several":[169],"examples,":[170],"direct":[172],"readers":[174],"recent":[176],"work":[177],"provides":[179],"solution":[181],"its":[188],"extensions.":[189],"assume":[191],"given":[198],"us":[200],"do":[202],"not":[203],"address":[204],"learning":[208],"it":[209],"data":[211],"statistical":[215],"inference":[216],"testing":[218],"issues.":[219]},"counts_by_year":[{"year":2020,"cited_by_count":1},{"year":2019,"cited_by_count":1}],"updated_date":"2026-07-29T09:40:50.615796","created_date":"2025-10-10T00:00:00"}