{"id":"https://openalex.org/W4409149166","doi":"https://doi.org/10.1145/3690624.3709345","title":"Dynamic Causal Structure Discovery and Causal Effect Estimation","display_name":"Dynamic Causal Structure Discovery and Causal Effect Estimation","publication_year":2025,"publication_date":"2025-04-04","ids":{"openalex":"https://openalex.org/W4409149166","doi":"https://doi.org/10.1145/3690624.3709345"},"language":"en","primary_location":{"id":"doi:10.1145/3690624.3709345","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3690624.3709345","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.1","raw_type":"proceedings-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/A5026869559","display_name":"Jianian Wang","orcid":null},"institutions":[{"id":"https://openalex.org/I137902535","display_name":"North Carolina State University","ror":"https://ror.org/04tj63d06","country_code":"US","type":"education","lineage":["https://openalex.org/I137902535"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Jianian Wang","raw_affiliation_strings":["North Carolina State University, Raleigh, NC, USA"],"affiliations":[{"raw_affiliation_string":"North Carolina State University, Raleigh, NC, USA","institution_ids":["https://openalex.org/I137902535"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5089012283","display_name":"Rui Song","orcid":"https://orcid.org/0000-0003-1875-2115"},"institutions":[{"id":"https://openalex.org/I137902535","display_name":"North Carolina State University","ror":"https://ror.org/04tj63d06","country_code":"US","type":"education","lineage":["https://openalex.org/I137902535"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Rui Song","raw_affiliation_strings":["North Carolina State University, Raleigh, NC, USA"],"affiliations":[{"raw_affiliation_string":"North Carolina State University, Raleigh, NC, USA","institution_ids":["https://openalex.org/I137902535"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5026869559"],"corresponding_institution_ids":["https://openalex.org/I137902535"],"apc_list":null,"apc_paid":null,"fwci":2.8843,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.90226243,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":91,"max":95},"biblio":{"volume":null,"issue":null,"first_page":"1433","last_page":"1444"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11303","display_name":"Bayesian Modeling and Causal Inference","score":0.9983999729156494,"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":0.9983999729156494,"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":0.9641000032424927,"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/T11710","display_name":"Biomedical Text Mining and Ontologies","score":0.9142000079154968,"subfield":{"id":"https://openalex.org/subfields/1312","display_name":"Molecular Biology"},"field":{"id":"https://openalex.org/fields/13","display_name":"Biochemistry, Genetics and Molecular Biology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/causal-structure","display_name":"Causal structure","score":0.7561585903167725},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6123263835906982},{"id":"https://openalex.org/keywords/estimation","display_name":"Estimation","score":0.5726504325866699},{"id":"https://openalex.org/keywords/causal-model","display_name":"Causal model","score":0.48911839723587036},{"id":"https://openalex.org/keywords/econometrics","display_name":"Econometrics","score":0.3742121160030365},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3601175844669342},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.18657973408699036},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.15900662541389465},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.1350513994693756}],"concepts":[{"id":"https://openalex.org/C163504300","wikidata":"https://www.wikidata.org/wiki/Q2364925","display_name":"Causal structure","level":2,"score":0.7561585903167725},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6123263835906982},{"id":"https://openalex.org/C96250715","wikidata":"https://www.wikidata.org/wiki/Q965330","display_name":"Estimation","level":2,"score":0.5726504325866699},{"id":"https://openalex.org/C11671645","wikidata":"https://www.wikidata.org/wiki/Q5054567","display_name":"Causal model","level":2,"score":0.48911839723587036},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.3742121160030365},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3601175844669342},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.18657973408699036},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.15900662541389465},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.1350513994693756},{"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/C201995342","wikidata":"https://www.wikidata.org/wiki/Q682496","display_name":"Systems engineering","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3690624.3709345","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3690624.3709345","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.1","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":24,"referenced_works":["https://openalex.org/W600974805","https://openalex.org/W2069541127","https://openalex.org/W2075200608","https://openalex.org/W2095690451","https://openalex.org/W2099900459","https://openalex.org/W2124852332","https://openalex.org/W2132507555","https://openalex.org/W2134240743","https://openalex.org/W2151226328","https://openalex.org/W2155573334","https://openalex.org/W2226634768","https://openalex.org/W2509991828","https://openalex.org/W2606866400","https://openalex.org/W2618817116","https://openalex.org/W2740437707","https://openalex.org/W2768308213","https://openalex.org/W2895046720","https://openalex.org/W2990138404","https://openalex.org/W3006419771","https://openalex.org/W3011486546","https://openalex.org/W3098606038","https://openalex.org/W3104877591","https://openalex.org/W3165473472","https://openalex.org/W4382239756"],"related_works":["https://openalex.org/W2161504683","https://openalex.org/W2093587551","https://openalex.org/W2477954850","https://openalex.org/W4307313254","https://openalex.org/W2740541622","https://openalex.org/W2784306284","https://openalex.org/W2124859246","https://openalex.org/W1985230145","https://openalex.org/W3169419898","https://openalex.org/W4386620154"],"abstract_inverted_index":{"To":[0],"represent":[1],"the":[2,34,50,74,79,89,94,101,108,142,150,153,159,163,172],"causal":[3,26,36,51,76,80,96,105,119,143,168],"relationships":[4,120],"between":[5],"variables,":[6],"a":[7,46,69],"directed":[8],"acyclic":[9],"graph":[10,77],"(DAG)":[11],"is":[12],"widely":[13],"utilized":[14],"in":[15,61],"many":[16],"areas,":[17],"such":[18],"as":[19],"social":[20],"sciences,":[21],"epidemics,":[22],"and":[23,117,138,145,166],"genetics.":[24],"Many":[25],"structure":[27,37],"learning":[28,40],"approaches":[29,44],"are":[30,82],"developed":[31],"to":[32,72,84,99,124,148],"learn":[33],"hidden":[35,47],"using":[38],"deep":[39],"approaches.":[41],"However,":[42],"these":[43],"have":[45],"assumption":[48],"that":[49,132],"relationship":[52],"remains":[53],"unchanged":[54],"over":[55],"time,":[56],"which":[57],"may":[58],"not":[59],"hold":[60],"real":[62],"life.":[63],"In":[64],"this":[65],"paper,":[66],"we":[67,112],"develop":[68],"new":[70],"framework":[71],"model":[73,110],"dynamic":[75,102],"where":[78],"relations":[81],"allowed":[83],"be":[85],"time-varying.":[86],"We":[87,128,156],"incorporate":[88],"basis":[90],"approximation":[91],"method":[92,161],"into":[93],"score-based":[95],"discovery":[97],"approach":[98],"capture":[100,114],"pattern":[103],"of":[104,152,174],"graphs.":[106],"Utilizing":[107],"autoregressive":[109],"structure,":[111],"could":[113,133],"both":[115,135],"contemporaneous":[116],"time-lagged":[118],"while":[121],"allowing":[122],"them":[123],"vary":[125],"with":[126],"time.":[127],"propose":[129],"an":[130],"algorithm":[131],"provide":[134,167],"past-time":[136],"estimates":[137,169],"future-time":[139],"predictions":[140],"on":[141,170],"graphs,":[144],"conduct":[146],"simulations":[147],"demonstrate":[149],"usefulness":[151],"proposed":[154,160],"method.":[155],"also":[157],"apply":[158],"for":[162],"covid-data":[164],"analysis,":[165],"how":[171],"effect":[173],"policy":[175],"restriction":[176],"changes.":[177]},"counts_by_year":[{"year":2025,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
