{"id":"https://openalex.org/W4389987388","doi":"https://doi.org/10.1109/jsait.2023.3342569","title":"Causal Graph Discovery From Self and Mutually Exciting Time Series","display_name":"Causal Graph Discovery From Self and Mutually Exciting Time Series","publication_year":2023,"publication_date":"2023-01-01","ids":{"openalex":"https://openalex.org/W4389987388","doi":"https://doi.org/10.1109/jsait.2023.3342569"},"language":"en","primary_location":{"id":"doi:10.1109/jsait.2023.3342569","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/jsait.2023.3342569","pdf_url":null,"source":{"id":"https://openalex.org/S4210211895","display_name":"IEEE Journal on Selected Areas in Information Theory","issn_l":"2641-8770","issn":["2641-8770"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Journal on Selected Areas in Information Theory","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/A5013412723","display_name":"Song Wei","orcid":"https://orcid.org/0000-0001-7725-4269"},"institutions":[{"id":"https://openalex.org/I130701444","display_name":"Georgia Institute of Technology","ror":"https://ror.org/01zkghx44","country_code":"US","type":"education","lineage":["https://openalex.org/I130701444"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Song Wei","raw_affiliation_strings":["School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, GA, USA"],"affiliations":[{"raw_affiliation_string":"School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, GA, USA","institution_ids":["https://openalex.org/I130701444"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5047736740","display_name":"Yao Xie","orcid":"https://orcid.org/0000-0001-6777-2951"},"institutions":[{"id":"https://openalex.org/I130701444","display_name":"Georgia Institute of Technology","ror":"https://ror.org/01zkghx44","country_code":"US","type":"education","lineage":["https://openalex.org/I130701444"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yao Xie","raw_affiliation_strings":["School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, GA, USA"],"affiliations":[{"raw_affiliation_string":"School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, GA, USA","institution_ids":["https://openalex.org/I130701444"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5028464504","display_name":"Christopher S. Josef","orcid":"https://orcid.org/0000-0001-9368-6926"},"institutions":[{"id":"https://openalex.org/I150468666","display_name":"Emory University","ror":"https://ror.org/03czfpz43","country_code":"US","type":"education","lineage":["https://openalex.org/I150468666"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Christopher S. Josef","raw_affiliation_strings":["Department of Surgery, Emory University School of Medicine, Atlanta, GA, USA"],"affiliations":[{"raw_affiliation_string":"Department of Surgery, Emory University School of Medicine, Atlanta, GA, USA","institution_ids":["https://openalex.org/I150468666"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5012511062","display_name":"Rishikesan Kamaleswaran","orcid":"https://orcid.org/0000-0001-8366-4811"},"institutions":[{"id":"https://openalex.org/I150468666","display_name":"Emory University","ror":"https://ror.org/03czfpz43","country_code":"US","type":"education","lineage":["https://openalex.org/I150468666"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Rishikesan Kamaleswaran","raw_affiliation_strings":["Department of Biomedical Informatics, Emory University School of Medicine, Atlanta, GA, USA"],"affiliations":[{"raw_affiliation_string":"Department of Biomedical Informatics, Emory University School of Medicine, Atlanta, GA, USA","institution_ids":["https://openalex.org/I150468666"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5013412723"],"corresponding_institution_ids":["https://openalex.org/I130701444"],"apc_list":null,"apc_paid":null,"fwci":0.5237,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.72927553,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":96,"max":97},"biblio":{"volume":"4","issue":null,"first_page":"747","last_page":"761"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11303","display_name":"Bayesian Modeling and Causal Inference","score":0.9972000122070312,"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":0.9972000122070312,"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/T12205","display_name":"Time Series Analysis and Forecasting","score":0.9927999973297119,"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"}},{"id":"https://openalex.org/T10799","display_name":"Data Visualization and Analytics","score":0.9785000085830688,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/series","display_name":"Series (stratigraphy)","score":0.5893129706382751},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.4912511706352234},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.4765130281448364},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.32384711503982544},{"id":"https://openalex.org/keywords/biology","display_name":"Biology","score":0.13256773352622986},{"id":"https://openalex.org/keywords/paleontology","display_name":"Paleontology","score":0.05822545289993286}],"concepts":[{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.5893129706382751},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4912511706352234},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.4765130281448364},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.32384711503982544},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.13256773352622986},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.05822545289993286}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/jsait.2023.3342569","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/jsait.2023.3342569","pdf_url":null,"source":{"id":"https://openalex.org/S4210211895","display_name":"IEEE Journal on Selected Areas in Information Theory","issn_l":"2641-8770","issn":["2641-8770"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Journal on Selected Areas in Information Theory","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G443950390","display_name":null,"funder_award_id":"CMMI-2112533","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G4477870991","display_name":null,"funder_award_id":"DMS-1830210","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G4996304644","display_name":null,"funder_award_id":"DMS-1938106","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G76754878","display_name":null,"funder_award_id":"NSF DMS-2134037","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G7799450740","display_name":null,"funder_award_id":"CMMI-2015787","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G855777396","display_name":null,"funder_award_id":"CCF-1650913","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"}],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"},{"id":"https://openalex.org/F4320308599","display_name":"Coca-Cola Foundation","ror":"https://ror.org/00yc8qw13"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":63,"referenced_works":["https://openalex.org/W1580826879","https://openalex.org/W2020925091","https://openalex.org/W2044005979","https://openalex.org/W2068000292","https://openalex.org/W2099900459","https://openalex.org/W2109605413","https://openalex.org/W2143891888","https://openalex.org/W2149986825","https://openalex.org/W2151226328","https://openalex.org/W2154195147","https://openalex.org/W2178935672","https://openalex.org/W2280404143","https://openalex.org/W2614741637","https://openalex.org/W2786619647","https://openalex.org/W2904730083","https://openalex.org/W2907768713","https://openalex.org/W2944325860","https://openalex.org/W2947197489","https://openalex.org/W2947626232","https://openalex.org/W2948579453","https://openalex.org/W2962888970","https://openalex.org/W2980581521","https://openalex.org/W2992764683","https://openalex.org/W2995282027","https://openalex.org/W3000219431","https://openalex.org/W3017330193","https://openalex.org/W3046153892","https://openalex.org/W3048074762","https://openalex.org/W3097273900","https://openalex.org/W3107467812","https://openalex.org/W3131712583","https://openalex.org/W3133932964","https://openalex.org/W3135588948","https://openalex.org/W3201412947","https://openalex.org/W3217360279","https://openalex.org/W4237742758","https://openalex.org/W4254526633","https://openalex.org/W4288095810","https://openalex.org/W4309908049","https://openalex.org/W4377235195","https://openalex.org/W4385568531","https://openalex.org/W4393380087","https://openalex.org/W6628742764","https://openalex.org/W6631593755","https://openalex.org/W6638669148","https://openalex.org/W6676197543","https://openalex.org/W6682361391","https://openalex.org/W6683775299","https://openalex.org/W6730091202","https://openalex.org/W6747916571","https://openalex.org/W6754506371","https://openalex.org/W6761805307","https://openalex.org/W6762856758","https://openalex.org/W6763168094","https://openalex.org/W6768934218","https://openalex.org/W6771102817","https://openalex.org/W6773860202","https://openalex.org/W6780392805","https://openalex.org/W6803404721","https://openalex.org/W6804017524","https://openalex.org/W6842010470","https://openalex.org/W6842507390","https://openalex.org/W6849125000"],"related_works":["https://openalex.org/W2748952813","https://openalex.org/W2390279801","https://openalex.org/W2358668433","https://openalex.org/W2376932109","https://openalex.org/W2001405890","https://openalex.org/W2382290278","https://openalex.org/W2478288626","https://openalex.org/W4391913857","https://openalex.org/W2350741829","https://openalex.org/W2530322880"],"abstract_inverted_index":{"We":[0,75],"present":[1],"a":[2,10,27,43,50,59,67],"generalized":[3],"linear":[4,13,60],"structural":[5],"causal":[6,17,39,105],"model,":[7],"coupled":[8],"with":[9],"novel":[11],"data-adaptive":[12],"regularization,":[14],"to":[15,62,117],"recover":[16],"directed":[18],"acyclic":[19],"graphs":[20],"(DAGs)":[21],"from":[22],"time":[23],"series.":[24],"By":[25],"leveraging":[26],"recently":[28],"developed":[29],"stochastic":[30],"monotone":[31,72],"Variational":[32],"Inequality":[33],"(VI)":[34],"formulation,":[35],"we":[36,48,94],"cast":[37],"the":[38,82,96],"discovery":[40],"problem":[41],"as":[42,122],"general":[44],"convex":[45],"optimization.":[46],"Furthermore,":[47],"develop":[49],"non-asymptotic":[51],"recovery":[52],"guarantee":[53],"and":[54,80],"quantifiable":[55],"uncertainty":[56],"by":[57],"solving":[58],"program":[61],"establish":[63],"confidence":[64],"intervals":[65],"for":[66],"wide":[68],"range":[69],"of":[70,85,98],"non-linear":[71],"link":[73],"functions.":[74],"validate":[76],"our":[77,86,99],"theoretical":[78],"results":[79],"show":[81],"competitive":[83],"performance":[84,116],"method":[87],"via":[88],"extensive":[89],"numerical":[90],"experiments.":[91],"Most":[92],"importantly,":[93],"demonstrate":[95],"effectiveness":[97],"approach":[100],"in":[101],"recovering":[102],"highly":[103],"interpretable":[104],"DAGs":[106],"over":[107],"Sepsis":[108],"Associated":[109],"Derangements":[110],"(SADs)":[111],"while":[112],"achieving":[113],"comparable":[114],"prediction":[115],"powerful":[118],"\u201cblack-box\u201d":[119],"models":[120],"such":[121],"XGBoost.":[123]},"counts_by_year":[{"year":2023,"cited_by_count":3}],"updated_date":"2025-12-25T23:11:45.687758","created_date":"2025-10-10T00:00:00"}