{"id":"https://openalex.org/W4391305732","doi":"https://doi.org/10.1109/tnnls.2024.3352657","title":"Linear Deconfounded Score Method: Scoring DAGs With Dense Unobserved Confounding","display_name":"Linear Deconfounded Score Method: Scoring DAGs With Dense Unobserved Confounding","publication_year":2024,"publication_date":"2024-01-29","ids":{"openalex":"https://openalex.org/W4391305732","doi":"https://doi.org/10.1109/tnnls.2024.3352657","pmid":"https://pubmed.ncbi.nlm.nih.gov/38285579"},"language":"en","primary_location":{"id":"doi:10.1109/tnnls.2024.3352657","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tnnls.2024.3352657","pdf_url":null,"source":{"id":"https://openalex.org/S4210175523","display_name":"IEEE Transactions on Neural Networks and Learning Systems","issn_l":"2162-237X","issn":["2162-237X","2162-2388"],"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 Transactions on Neural Networks and Learning Systems","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","pubmed"],"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/A5038898466","display_name":"Alexis Bellot","orcid":"https://orcid.org/0000-0003-4665-7748"},"institutions":[{"id":"https://openalex.org/I241749","display_name":"University of Cambridge","ror":"https://ror.org/013meh722","country_code":"GB","type":"education","lineage":["https://openalex.org/I241749"]},{"id":"https://openalex.org/I4210090411","display_name":"Google DeepMind (United Kingdom)","ror":"https://ror.org/00971b260","country_code":"GB","type":"company","lineage":["https://openalex.org/I4210090411","https://openalex.org/I4210128969"]},{"id":"https://openalex.org/I4210113297","display_name":"Google (United Kingdom)","ror":"https://ror.org/024bc3e07","country_code":"GB","type":"company","lineage":["https://openalex.org/I1291425158","https://openalex.org/I4210113297","https://openalex.org/I4210128969"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Alexis Bellot","raw_affiliation_strings":["Department of Applied Mathematics and Theoretical Physics, University of Cambridge, Cambridge, U.K","Google DeepMind, London, U.K"],"raw_orcid":"https://orcid.org/0000-0003-4665-7748","affiliations":[{"raw_affiliation_string":"Department of Applied Mathematics and Theoretical Physics, University of Cambridge, Cambridge, U.K","institution_ids":["https://openalex.org/I241749"]},{"raw_affiliation_string":"Google DeepMind, London, U.K","institution_ids":["https://openalex.org/I4210090411","https://openalex.org/I4210113297"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5012339002","display_name":"Mihaela van der Schaar","orcid":"https://orcid.org/0000-0003-3933-6049"},"institutions":[{"id":"https://openalex.org/I161318765","display_name":"University of California, Los Angeles","ror":"https://ror.org/046rm7j60","country_code":"US","type":"education","lineage":["https://openalex.org/I161318765"]},{"id":"https://openalex.org/I241749","display_name":"University of Cambridge","ror":"https://ror.org/013meh722","country_code":"GB","type":"education","lineage":["https://openalex.org/I241749"]}],"countries":["GB","US"],"is_corresponding":false,"raw_author_name":"Mihaela van der Schaar","raw_affiliation_strings":["Department of Applied Mathematics and Theoretical Physics, University of Cambridge, Cambridge, U.K","Electrical and Computer Engineering Department, University of California at Los Angeles, Los Angeles, CA, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Applied Mathematics and Theoretical Physics, University of Cambridge, Cambridge, U.K","institution_ids":["https://openalex.org/I241749"]},{"raw_affiliation_string":"Electrical and Computer Engineering Department, University of California at Los Angeles, Los Angeles, CA, USA","institution_ids":["https://openalex.org/I161318765"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":1.2172,"has_fulltext":false,"cited_by_count":5,"citation_normalized_percentile":{"value":0.80754232,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":97},"biblio":{"volume":"35","issue":"4","first_page":"4948","last_page":"4962"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11303","display_name":"Bayesian Modeling and Causal Inference","score":0.9994999766349792,"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":0.9994999766349792,"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/T10050","display_name":"Multi-Criteria Decision Making","score":0.9465000033378601,"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/T10845","display_name":"Advanced Causal Inference Techniques","score":0.942300021648407,"subfield":{"id":"https://openalex.org/subfields/2613","display_name":"Statistics and Probability"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/spurious-relationship","display_name":"Spurious relationship","score":0.8164801597595215},{"id":"https://openalex.org/keywords/directed-acyclic-graph","display_name":"Directed acyclic graph","score":0.772761344909668},{"id":"https://openalex.org/keywords/confounding","display_name":"Confounding","score":0.6782746911048889},{"id":"https://openalex.org/keywords/causal-inference","display_name":"Causal inference","score":0.540904700756073},{"id":"https://openalex.org/keywords/observational-study","display_name":"Observational study","score":0.5265132188796997},{"id":"https://openalex.org/keywords/causal-structure","display_name":"Causal structure","score":0.5158704519271851},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.49966859817504883},{"id":"https://openalex.org/keywords/causal-model","display_name":"Causal model","score":0.49933886528015137},{"id":"https://openalex.org/keywords/causality","display_name":"Causality (physics)","score":0.4886396527290344},{"id":"https://openalex.org/keywords/econometrics","display_name":"Econometrics","score":0.45989564061164856},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.4284003674983978},{"id":"https://openalex.org/keywords/generalized-linear-model","display_name":"Generalized linear model","score":0.4114791750907898},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.3622381091117859},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3466447591781616},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.3372229337692261},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.29383689165115356}],"concepts":[{"id":"https://openalex.org/C97256817","wikidata":"https://www.wikidata.org/wiki/Q1462316","display_name":"Spurious relationship","level":2,"score":0.8164801597595215},{"id":"https://openalex.org/C74197172","wikidata":"https://www.wikidata.org/wiki/Q1195339","display_name":"Directed acyclic graph","level":2,"score":0.772761344909668},{"id":"https://openalex.org/C77350462","wikidata":"https://www.wikidata.org/wiki/Q1125472","display_name":"Confounding","level":2,"score":0.6782746911048889},{"id":"https://openalex.org/C158600405","wikidata":"https://www.wikidata.org/wiki/Q5054566","display_name":"Causal inference","level":2,"score":0.540904700756073},{"id":"https://openalex.org/C23131810","wikidata":"https://www.wikidata.org/wiki/Q818574","display_name":"Observational study","level":2,"score":0.5265132188796997},{"id":"https://openalex.org/C163504300","wikidata":"https://www.wikidata.org/wiki/Q2364925","display_name":"Causal structure","level":2,"score":0.5158704519271851},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.49966859817504883},{"id":"https://openalex.org/C11671645","wikidata":"https://www.wikidata.org/wiki/Q5054567","display_name":"Causal model","level":2,"score":0.49933886528015137},{"id":"https://openalex.org/C64357122","wikidata":"https://www.wikidata.org/wiki/Q1149766","display_name":"Causality (physics)","level":2,"score":0.4886396527290344},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.45989564061164856},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.4284003674983978},{"id":"https://openalex.org/C41587187","wikidata":"https://www.wikidata.org/wiki/Q1501882","display_name":"Generalized linear model","level":2,"score":0.4114791750907898},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.3622381091117859},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3466447591781616},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3372229337692261},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.29383689165115356},{"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":2,"locations":[{"id":"doi:10.1109/tnnls.2024.3352657","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tnnls.2024.3352657","pdf_url":null,"source":{"id":"https://openalex.org/S4210175523","display_name":"IEEE Transactions on Neural Networks and Learning Systems","issn_l":"2162-237X","issn":["2162-237X","2162-2388"],"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 Transactions on Neural Networks and Learning Systems","raw_type":"journal-article"},{"id":"pmid:38285579","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/38285579","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":"IEEE transactions on neural networks and learning systems","raw_type":null}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G3078532643","display_name":"EAGER-DynamicData: Real-time Discovery and Timely Event Detection from Dynamic and Multi-Modal Data Streams","funder_award_id":"1462245","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G4500485203","display_name":"The Alan Turing Institute","funder_award_id":"EP/N510129/1","funder_id":"https://openalex.org/F4320334627","funder_display_name":"Engineering and Physical Sciences Research Council"},{"id":"https://openalex.org/G5147494120","display_name":"PFI:BIC Human-Centered Smart-Integration of Mobile Imaging and Sensing Tools with Machine Learning for Ubiquitous Quantification of Waterborne and Airborne Nanoparticles","funder_award_id":"1533983","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G8597009950","display_name":null,"funder_award_id":"EP/N510129/1","funder_id":"https://openalex.org/F4320334627","funder_display_name":"Engineering and Physical Sciences Research Council"}],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"},{"id":"https://openalex.org/F4320334627","display_name":"Engineering and Physical Sciences Research Council","ror":"https://ror.org/0439y7842"},{"id":"https://openalex.org/F4320337345","display_name":"Office of Naval Research","ror":"https://ror.org/00rk2pe57"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":79,"referenced_works":["https://openalex.org/W1523985187","https://openalex.org/W1587150650","https://openalex.org/W2008620264","https://openalex.org/W2014165366","https://openalex.org/W2036363967","https://openalex.org/W2040373108","https://openalex.org/W2073307618","https://openalex.org/W2086331397","https://openalex.org/W2096192437","https://openalex.org/W2099781564","https://openalex.org/W2099925654","https://openalex.org/W2109605413","https://openalex.org/W2127300249","https://openalex.org/W2134240743","https://openalex.org/W2134652049","https://openalex.org/W2137499573","https://openalex.org/W2143891888","https://openalex.org/W2145962650","https://openalex.org/W2151226328","https://openalex.org/W2163687466","https://openalex.org/W2168494960","https://openalex.org/W2181581468","https://openalex.org/W2215484510","https://openalex.org/W2606946190","https://openalex.org/W2747984964","https://openalex.org/W2784010521","https://openalex.org/W2885495226","https://openalex.org/W2886399985","https://openalex.org/W2900832492","https://openalex.org/W2954682669","https://openalex.org/W2963588899","https://openalex.org/W2964243609","https://openalex.org/W2964287564","https://openalex.org/W3004253194","https://openalex.org/W3007224510","https://openalex.org/W3036069262","https://openalex.org/W3049025113","https://openalex.org/W3099661174","https://openalex.org/W3103539622","https://openalex.org/W3123062862","https://openalex.org/W3123751029","https://openalex.org/W3124765495","https://openalex.org/W3157189937","https://openalex.org/W3197421332","https://openalex.org/W3203034578","https://openalex.org/W4205565683","https://openalex.org/W4225615968","https://openalex.org/W4247571494","https://openalex.org/W4287710800","https://openalex.org/W4297825638","https://openalex.org/W4298870021","https://openalex.org/W4302423442","https://openalex.org/W4307235029","https://openalex.org/W4388685409","https://openalex.org/W4393159907","https://openalex.org/W6635147717","https://openalex.org/W6675106608","https://openalex.org/W6676197543","https://openalex.org/W6679715006","https://openalex.org/W6679948214","https://openalex.org/W6682361391","https://openalex.org/W6683821031","https://openalex.org/W6685718980","https://openalex.org/W6688970714","https://openalex.org/W6734219640","https://openalex.org/W6737818077","https://openalex.org/W6746569983","https://openalex.org/W6748646278","https://openalex.org/W6752073546","https://openalex.org/W6754506371","https://openalex.org/W6756319660","https://openalex.org/W6765882099","https://openalex.org/W6767545809","https://openalex.org/W6767750290","https://openalex.org/W6780392805","https://openalex.org/W6784381004","https://openalex.org/W6788536977","https://openalex.org/W6802197981","https://openalex.org/W6858584090"],"related_works":["https://openalex.org/W4220807758","https://openalex.org/W2161504683","https://openalex.org/W4378718308","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"],"abstract_inverted_index":{"This":[0],"article":[1],"deals":[2],"with":[3,143],"the":[4,19,24,46,51,71,82,97,157],"discovery":[5,137],"of":[6,12,21,26,45,61,84],"causal":[7,72,107,136,163],"relations":[8],"from":[9],"a":[10,40,93,115,132],"combination":[11],"observational":[13],"data":[14,99],"and":[15,106,130,146,176],"qualitative":[16],"assumptions":[17],"about":[18],"nature":[20],"causality":[22],"in":[23,89,96,153,166,173],"presence":[25],"unmeasured":[27],"confounding.":[28],"We":[29,151],"focus":[30],"on":[31,43],"applications":[32],"where":[33],"unobserved":[34,87],"variables":[35,62,125],"are":[36,63,182],"known":[37],"to":[38,148,161,170,178],"have":[39],"widespread":[41],"effect":[42,73],"many":[44],"observed":[47,98,124],"ones,":[48],"which":[49],"makes":[50],"problem":[52],"particularly":[53],"difficult":[54],"for":[55,103],"constraint-based":[56],"methods,":[57],"because":[58],"most":[59],"pairs":[60],"conditionally":[64],"dependent":[65],"given":[66],"any":[67],"other":[68],"subset,":[69],"rendering":[70],"unidentifiable.":[74],"In":[75],"this":[76,90,110],"article,":[77],"we":[78,112,159],"show":[79],"that":[80,101,114,139,155],"under":[81],"principle":[83],"independent":[85],"mechanisms,":[86],"confounding":[88],"setting":[91],"leaves":[92],"statistical":[94],"footprint":[95],"distribution":[100],"allows":[102],"disentangling":[104],"spurious":[105],"effects.":[108],"Using":[109],"insight,":[111],"demonstrate":[113],"sparse":[116],"linear":[117],"Gaussian":[118],"directed":[119],"acyclic":[120],"graph":[121],"(DAG)":[122],"among":[123],"may":[126,140],"be":[127,141],"recovered":[128],"approximately":[129],"propose":[131],"simple":[133],"adjusted":[134],"score-based":[135],"algorithm":[138],"implemented":[142],"general-purpose":[144],"solvers":[145],"scales":[147],"high-dimensional":[149],"problems.":[150],"find,":[152],"addition,":[154],"despite":[156],"conditions":[158],"pose":[160],"guarantee":[162],"recovery,":[164],"performance":[165],"practice":[167],"is":[168],"robust":[169],"large":[171],"deviations":[172],"model":[174],"assumptions,":[175],"extensions":[177],"nonlinear":[179],"structural":[180],"models":[181],"possible.":[183]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":1}],"updated_date":"2026-07-22T07:51:19.307946","created_date":"2025-10-10T00:00:00"}