{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,18]],"date-time":"2026-06-18T10:39:40Z","timestamp":1781779180181,"version":"3.54.5"},"reference-count":62,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"4","license":[{"start":{"date-parts":[[2024,4,1]],"date-time":"2024-04-01T00:00:00Z","timestamp":1711929600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2024,4,1]],"date-time":"2024-04-01T00:00:00Z","timestamp":1711929600000},"content-version":"am","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2024,4,1]],"date-time":"2024-04-01T00:00:00Z","timestamp":1711929600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2024,4,1]],"date-time":"2024-04-01T00:00:00Z","timestamp":1711929600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/501100000266","name":"Alan Turing Institute through Engineering and Physical Sciences Research Council","doi-asserted-by":"publisher","award":["EP\/N510129\/1"],"award-info":[{"award-number":["EP\/N510129\/1"]}],"id":[{"id":"10.13039\/501100000266","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000006","name":"Office of Naval Research","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100000006","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000001","name":"NSF","doi-asserted-by":"publisher","award":["1462245"],"award-info":[{"award-number":["1462245"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000001","name":"NSF","doi-asserted-by":"publisher","award":["1533983"],"award-info":[{"award-number":["1533983"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Neural Netw. Learning Syst."],"published-print":{"date-parts":[[2024,4]]},"DOI":"10.1109\/tnnls.2024.3352657","type":"journal-article","created":{"date-parts":[[2024,1,29]],"date-time":"2024-01-29T18:42:53Z","timestamp":1706553773000},"page":"4948-4962","source":"Crossref","is-referenced-by-count":4,"title":["Linear Deconfounded Score Method: Scoring DAGs With Dense Unobserved Confounding"],"prefix":"10.1109","volume":"35","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4665-7748","authenticated-orcid":false,"given":"Alexis","family":"Bellot","sequence":"first","affiliation":[{"name":"Department of Applied Mathematics and Theoretical Physics, University of Cambridge, Cambridge, U.K."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mihaela","family":"van der Schaar","sequence":"additional","affiliation":[{"name":"Department of Applied Mathematics and Theoretical Physics, University of Cambridge, Cambridge, U.K."}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref1","first-page":"4450","article-title":"Globally optimal score-based learning of directed acyclic graphs in high-dimensions","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Aragam"},{"key":"ref2","article-title":"Learning directed acyclic graphs with penalized neighbourhood regression","author":"Aragam","year":"2015","journal-title":"arXiv:1511.08963"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1111\/1468-0262.00392"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1126\/science.286.5439.509"},{"key":"ref5","article-title":"Towards bounding causal effects under Markov equivalence","author":"Bellot","year":"2023","journal-title":"arXiv:2311.07259"},{"key":"ref6","first-page":"2199","article-title":"Conditional independence testing using generative adversarial networks","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Bellot"},{"key":"ref7","article-title":"Accounting for unobserved confounding in domain generalization","author":"Bellot","year":"2020","journal-title":"arXiv:2007.10653"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v38i10.28980"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/ICMLA.2019.00073"},{"key":"ref10","first-page":"557","article-title":"Group invariance principles for causal generative models","volume-title":"Proc. Int. Conf. Artif. Intell. Statist.","author":"Besserve"},{"key":"ref11","first-page":"2314","article-title":"Differentiable causal discovery under unmeasured confounding","volume-title":"Proc. Int. Conf. Artif. Intell. Statist.","author":"Bhattacharya"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1214\/19-AOS1877"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-20192-9"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2017.2734804"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1145\/1970392.1970395"},{"key":"ref16","article-title":"Spectral deconfounding via perturbed sparse linear models","author":"Cevid","year":"2018","journal-title":"arXiv:1811.05352"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.3386\/w0996"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2020.3045812"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1214\/16-AOS1434"},{"key":"ref20","first-page":"445","article-title":"Learning equivalence classes of Bayesian-network structures","volume":"2","author":"Chickering","year":"2002","journal-title":"J. Mach. Learn. Res."},{"key":"ref21","article-title":"Learning sparse causal models is not NP-hard","author":"Claassen","year":"2013","journal-title":"arXiv:1309.6824"},{"issue":"1","key":"ref22","first-page":"3741","article-title":"Order-independent constraint-based causal structure learning","volume":"15","author":"Colombo","year":"2014","journal-title":"J. Mach. Learn. Res."},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1214\/11-AOS940"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1111\/rssb.12016"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1214\/17-AOS1588"},{"key":"ref26","volume-title":"Macroeconomic shocks and the business cycle: Evidence from a structural factor model","author":"Forni","year":"2010"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1111\/rssb.12315"},{"key":"ref28","article-title":"Removing unwanted variation from high dimensional data with negative controls","author":"Gagnon-Bartsch","year":"2013"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1098\/rsta.2017.0237"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1201\/b18401"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1515\/jci-2017-0013"},{"key":"ref32","article-title":"Detecting non-causal artifacts in multivariate linear regression models","author":"Janzing","year":"2018","journal-title":"arXiv:1803.00810"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1038\/nrg2825"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pgen.0030161"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1137\/S0895479896298506"},{"issue":"1","key":"ref36","first-page":"3065","article-title":"High-dimensional learning of linear causal networks via inverse covariance estimation","volume":"15","author":"Loh","year":"2014","journal-title":"J. Mach. Learn. Res."},{"key":"ref37","article-title":"On the role of sparsity and DAG constraints for learning linear DAGs","author":"Ng","year":"2020","journal-title":"arXiv:2006.10201"},{"key":"ref38","first-page":"7","article-title":"Structure learning with bow-free acyclic path diagrams","volume":"1050","author":"Nowzohour","year":"2015","journal-title":"Stat"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1186\/1752-0509-1-37"},{"key":"ref40","first-page":"4036","article-title":"Learning independent causal mechanisms","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Parascandolo"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1017\/CBO9780511803161"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1214\/aos\/1031689015"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1214\/18-AOS1732"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1002\/cpa.20294"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1126\/science.1105809"},{"key":"ref46","first-page":"10","article-title":"Reverse engineering of the stress response during expression of a recombinant protein","volume-title":"Proc. EUNITE Symp.","author":"Schmidt-Heck"},{"key":"ref47","first-page":"416","article-title":"Who learns better Bayesian network structures: Constraint-based, score-based or hybrid algorithms?","volume-title":"Proc. Int. Conf. Probabilistic Graph. Models","author":"Scutari"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1111\/rssb.12359"},{"issue":"10","key":"ref49","first-page":"2003","article-title":"A linear non-Gaussian acyclic model for causal discovery","volume":"7","author":"Shimizu","year":"2006","journal-title":"J. Mach. Learn. Res."},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.1162\/154247603770383415"},{"key":"ref51","doi-asserted-by":"publisher","DOI":"10.7551\/mitpress\/1754.001.0001"},{"key":"ref52","first-page":"59","article-title":"Score-based vs constraint-based causal learning in the presence of confounders","volume-title":"Proc. CFA UAI","author":"Triantafillou"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijar.2018.08.002"},{"key":"ref54","doi-asserted-by":"publisher","DOI":"10.1109\/TIT.2009.2016018"},{"key":"ref55","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2021.3139389"},{"key":"ref56","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2022.3213641"},{"key":"ref57","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2019.2921613"},{"key":"ref58","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2021.3106111"},{"key":"ref59","first-page":"1437","article-title":"Causal reasoning with ancestral graphs","volume":"9","author":"Zhang","year":"2008","journal-title":"J. Mach. Learn. Res."},{"key":"ref60","doi-asserted-by":"publisher","DOI":"10.1016\/j.artint.2008.08.001"},{"key":"ref61","first-page":"9472","article-title":"Dags with no tears: Continuous optimization for structure learning","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Zheng"},{"key":"ref62","first-page":"3414","article-title":"Learning sparse nonparametric dags","volume-title":"Proc. Int. Conf. Artif. Intell. Statist.","author":"Zheng"}],"container-title":["IEEE Transactions on Neural Networks and Learning Systems"],"original-title":[],"link":[{"URL":"https:\/\/ieeexplore.ieee.org\/ielam\/5962385\/10492491\/10415545-aam.pdf","content-type":"application\/pdf","content-version":"am","intended-application":"syndication"},{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/5962385\/10492491\/10415545.pdf?arnumber=10415545","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,4,9]],"date-time":"2024-04-09T19:39:57Z","timestamp":1712691597000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10415545\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,4]]},"references-count":62,"journal-issue":{"issue":"4"},"URL":"https:\/\/doi.org\/10.1109\/tnnls.2024.3352657","relation":{},"ISSN":["2162-237X","2162-2388"],"issn-type":[{"value":"2162-237X","type":"print"},{"value":"2162-2388","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,4]]}}}