{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,10]],"date-time":"2026-04-10T10:07:04Z","timestamp":1775815624727,"version":"3.50.1"},"reference-count":59,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"8","license":[{"start":{"date-parts":[[2025,8,1]],"date-time":"2025-08-01T00:00:00Z","timestamp":1754006400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2025,8,1]],"date-time":"2025-08-01T00:00:00Z","timestamp":1754006400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2025,8,1]],"date-time":"2025-08-01T00:00:00Z","timestamp":1754006400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/501100000923","name":"Australian Research Council","doi-asserted-by":"publisher","award":["DP230101122"],"award-info":[{"award-number":["DP230101122"]}],"id":[{"id":"10.13039\/501100000923","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":[[2025,8]]},"DOI":"10.1109\/tnnls.2024.3512790","type":"journal-article","created":{"date-parts":[[2024,12,10]],"date-time":"2024-12-10T20:52:38Z","timestamp":1733863958000},"page":"14078-14091","source":"Crossref","is-referenced-by-count":6,"title":["Disentangled Representation Learning for Causal Inference With Instruments"],"prefix":"10.1109","volume":"36","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0383-1462","authenticated-orcid":false,"given":"Debo","family":"Cheng","sequence":"first","affiliation":[{"name":"UniSA STEM, University of South Australia, Adelaide, SA, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9023-1878","authenticated-orcid":false,"given":"Jiuyong","family":"Li","sequence":"additional","affiliation":[{"name":"UniSA STEM, University of South Australia, Adelaide, SA, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2843-5738","authenticated-orcid":false,"given":"Lin","family":"Liu","sequence":"additional","affiliation":[{"name":"UniSA STEM, University of South Australia, Adelaide, SA, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1748-5801","authenticated-orcid":false,"given":"Ziqi","family":"Xu","sequence":"additional","affiliation":[{"name":"School of Computing Technologies, RMIT University, Melbourne, VIC, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Weijia","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Information and Physical Sciences, University of Newcastle, Callaghan, NSW, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0794-0404","authenticated-orcid":false,"given":"Jixue","family":"Liu","sequence":"additional","affiliation":[{"name":"UniSA STEM, University of South Australia, Adelaide, SA, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9732-4313","authenticated-orcid":false,"given":"Thuc Duy","family":"Le","sequence":"additional","affiliation":[{"name":"UniSA STEM, University of South Australia, Adelaide, SA, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1017\/CBO9780511803161"},{"key":"ref2","volume-title":"Causal Inference","author":"Hern\u00e1n","year":"2010"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1016\/0197-2456(81)90056-8"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.7551\/mitpress\/1754.001.0001"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2020.3045812"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2021.3133337"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1097\/01.ede.0000222409.00878.37"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1017\/CBO9781139025751"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1080\/01621459.1995.10476535"},{"key":"ref10","first-page":"85","article-title":"Generalized instrumental variables","volume-title":"Proc. 18th Conf. Uncertainty Artif. Intell.","author":"Brito"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1515\/em-2018-0024"},{"key":"ref12","first-page":"435","article-title":"On the testability of causal models with latent and instrumental variables","volume-title":"Proc. 11th Conf. Uncertainty Artif. Intell.","author":"Pearl"},{"issue":"1","key":"ref13","first-page":"120:1","article-title":"Learning instrumental variables with structural and non-Gaussianity assumptions","volume":"18","author":"Silva","year":"2017","journal-title":"J. Mach. Learn. Res."},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1080\/01621459.2014.994705"},{"key":"ref15","first-page":"190","article-title":"Instrumental variable tests for directed acyclic graph models","volume-title":"Proc. Int. Conf. Artif. Intell. Statist.","author":"Kuroki"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.3390\/e24040512"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.2307\/2648118"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1093\/biomet\/asy038"},{"key":"ref19","first-page":"1","article-title":"Learning disentangled representations for counterfactual regression","volume-title":"Proc. 8th Int. Conf. Learn. Represent.","author":"Hassanpour"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i12.17304"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1080\/01621459.2020.1783272"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1111\/ectj.12097"},{"key":"ref23","first-page":"15167","article-title":"Machine learning estimation of heterogeneous treatment effects with instruments","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"32","author":"Syrgkanis"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1093\/ije\/29.4.722"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1097\/01.ede.0000215160.88317.cb"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1214\/aos\/1031689015"},{"key":"ref27","first-page":"6446","article-title":"Causal effect inference with deep latent-variable models","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"30","author":"Louizos"},{"key":"ref28","first-page":"1","article-title":"Auto-encoding variational Bayes","volume-title":"Proc. 2nd Int. Conf. Learn. Represent.","author":"Kingma"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1561\/2200000056"},{"key":"ref30","first-page":"5399","article-title":"Orthogonality-promoting distance metric learning: Convex relaxation and theoretical analysis","volume-title":"Proc. 35th Int. Conf. Mach. Learn.","author":"Xie"},{"key":"ref31","first-page":"4114","article-title":"Challenging common assumptions in the unsupervised learning of disentangled representations","volume-title":"Proc. Int. Conf. Mach. Learn. (ICML)","author":"Locatello"},{"key":"ref32","first-page":"2207","article-title":"Variational autoencoders and nonlinear ICA: A unifying framework","volume-title":"Proc. 23rd Int. Conf. Artif. Intell. Statist. (AISTATS)","author":"Khemakhem"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1214\/18-AOS1709"},{"key":"ref34","first-page":"1414","article-title":"Deep IV: A flexible approach for counterfactual prediction","volume-title":"Proc. 34th Int. Conf. Mach. Learn.","author":"Hartford"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.5555\/3454287.3455008"},{"issue":"28","key":"ref36","first-page":"28:1","article-title":"Pyro: Deep universal probabilistic programming","volume":"20","author":"Bingham","year":"2019","journal-title":"J. Mach. Learn. Res."},{"key":"ref37","volume-title":"EconML: A Python Package for ML-Based Heterogeneous Treatment Effects Estimation","author":"Battocchi","year":"2019"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2022\/671"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1093\/biostatistics\/kxx057"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.3386\/w4483"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1126\/science.1105809"},{"key":"ref42","volume-title":"A Guide To Modern Econometrics","author":"Verbeek","year":"2008"},{"key":"ref43","volume-title":"MNIST Handwritten Digit Database","author":"LeCun","year":"2010"},{"key":"ref44","first-page":"4207","article-title":"A critical look at the consistency of causal estimation with deep latent variable models","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"34","author":"Rissanen"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1145\/3494568"},{"key":"ref46","first-page":"3559","article-title":"Deep generalized method of moments for instrumental variable analysis","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"32","author":"Bennett"},{"key":"ref47","first-page":"4595","article-title":"Kernel instrumental variable regression","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"32","author":"Singh"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1093\/ije\/dyt093"},{"key":"ref49","first-page":"398","article-title":"Ivy: Instrumental variable synthesis for causal inference","volume-title":"Proc. 23rd Int. Conf. Artif. Intell. Statist.","author":"Kuang"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.1111\/rssb.12275"},{"key":"ref51","first-page":"4096","article-title":"Valid causal inference with (some) invalid instruments","volume-title":"Proc. 38th Int. Conf. Mach. Learn.","author":"Hartford"},{"key":"ref52","first-page":"3020","article-title":"Learning representations for counterfactual inference","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Johansson"},{"key":"ref53","first-page":"3076","article-title":"Estimating individual treatment effect: Generalization bounds and algorithms","volume-title":"Proc. 34th Int. Conf. Mach. Learn.","author":"Shalit"},{"key":"ref54","first-page":"1","article-title":"GANITE: Estimation of individualized treatment effects using generative adversarial nets","volume-title":"Proc. 6th Int. Conf. Learn. Represent.","author":"Yoon"},{"key":"ref55","doi-asserted-by":"publisher","DOI":"10.1145\/3636423"},{"key":"ref56","article-title":"An introduction to proximal causal learning","author":"Tchetgen","year":"2020","journal-title":"arXiv:2009.10982"},{"key":"ref57","doi-asserted-by":"publisher","DOI":"10.1145\/3383313.3412225"},{"key":"ref58","doi-asserted-by":"publisher","DOI":"10.1162\/tacl_a_00511"},{"key":"ref59","doi-asserted-by":"publisher","DOI":"10.1109\/JPROC.2021.3058954"}],"container-title":["IEEE Transactions on Neural Networks and Learning Systems"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/5962385\/11114436\/10791303.pdf?arnumber=10791303","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,8,6]],"date-time":"2025-08-06T18:00:58Z","timestamp":1754503258000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10791303\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,8]]},"references-count":59,"journal-issue":{"issue":"8"},"URL":"https:\/\/doi.org\/10.1109\/tnnls.2024.3512790","relation":{},"ISSN":["2162-237X","2162-2388"],"issn-type":[{"value":"2162-237X","type":"print"},{"value":"2162-2388","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,8]]}}}