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Estimating long-term heterogeneous dose-response curve: Generalization bound leveraging optimal transport weights. arXiv: 2406.19195."},{"key":"10.1016\/j.neunet.2026.109144_bib0068","series-title":"6th international conference on learning representations, ICLR 2018","article-title":"GANITE: Estimation of individualized treatment effects using generative adversarial nets","author":"Yoon","year":"2018"},{"key":"10.1016\/j.neunet.2026.109144_bib0069","series-title":"Forty-first international conference on machine learning, ICML 2024","first-page":"58306","article-title":"Continuous treatment effects with surrogate outcomes","author":"Zeng","year":"2024"},{"issue":"1","key":"10.1016\/j.neunet.2026.109144_bib0070","doi-asserted-by":"crossref","first-page":"103","DOI":"10.1002\/sta.411","article-title":"Estimating optimal treatment regimes from a classification perspective","volume":"1","author":"Zhang","year":"2012","journal-title":"Statistical"},{"issue":"12","key":"10.1016\/j.neunet.2026.109144_bib0071","doi-asserted-by":"crossref","first-page":"5586","DOI":"10.1109\/TKDE.2021.3070203","article-title":"A survey on multi-task learning","volume":"34","author":"Zhang","year":"2022","journal-title":"IEEE Transactions on Knowledge and Data Engineering"},{"issue":"8","key":"10.1016\/j.neunet.2026.109144_bib0072","first-page":"8358","article-title":"Personalized dynamic counter ad-blocking using deep learning","volume":"35","author":"Zhao","year":"2023","journal-title":"IEEE Transactions on Knowledge and Data Engineering"},{"key":"10.1016\/j.neunet.2026.109144_bib0073","doi-asserted-by":"crossref","DOI":"10.1016\/j.neunet.2024.106147","article-title":"CI-GNN: A granger causality-inspired graph neural network for interpretable brain network-based psychiatric diagnosis","volume":"172","author":"Zheng","year":"2024","journal-title":"Neural Networks"},{"issue":"1","key":"10.1016\/j.neunet.2026.109144_bib0074","doi-asserted-by":"crossref","DOI":"10.1080\/2330443X.2023.2267617","article-title":"Synthetic control analysis of the short-term impact of New York state\u2019s bail elimination act on aggregate crime","volume":"11","author":"Zhou","year":"2023","journal-title":"Statistics and Public Policy"},{"issue":"4","key":"10.1016\/j.neunet.2026.109144_bib0075","doi-asserted-by":"crossref","first-page":"257","DOI":"10.1109\/4235.797969","article-title":"Multiobjective evolutionary algorithms: A comparative case study and the strength pareto approach","volume":"3","author":"Zitzler","year":"1999","journal-title":"IEEE Transactions on Evolutionary Computation"}],"container-title":["Neural Networks"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0893608026006052?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0893608026006052?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,5,28]],"date-time":"2026-05-28T17:09:34Z","timestamp":1779988174000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0893608026006052"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,11]]},"references-count":75,"alternative-id":["S0893608026006052"],"URL":"https:\/\/doi.org\/10.1016\/j.neunet.2026.109144","relation":{},"ISSN":["0893-6080"],"issn-type":[{"value":"0893-6080","type":"print"}],"subject":[],"published":{"date-parts":[[2026,11]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Pareto-optimal estimation and policy learning for balancing short-term and long-term outcomes","name":"articletitle","label":"Article Title"},{"value":"Neural Networks","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.neunet.2026.109144","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier Ltd. 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