{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,17]],"date-time":"2026-07-17T18:04:16Z","timestamp":1784311456044,"version":"3.55.0"},"reference-count":48,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T00:00:00Z","timestamp":1793491200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T00:00:00Z","timestamp":1793491200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,7,14]],"date-time":"2026-07-14T00:00:00Z","timestamp":1783987200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Neurocomputing"],"published-print":{"date-parts":[[2026,11]]},"DOI":"10.1016\/j.neucom.2026.134494","type":"journal-article","created":{"date-parts":[[2026,7,14]],"date-time":"2026-07-14T23:26:58Z","timestamp":1784071618000},"page":"134494","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["MTMT: Tiered treatment effect decomposition for multi-task uplift modeling"],"prefix":"10.1016","volume":"700","author":[{"given":"Yuxiang","family":"Wei","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhaoxin","family":"Qiu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yingjie","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuke","family":"Sun","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaoling","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.neucom.2026.134494_bib0005","series-title":"Proceedings of the ACM Web Conference 2022","first-page":"2310","article-title":"Lbcf: a large-scale budget-constrained causal forest algorithm","author":"Ai","year":"2022"},{"key":"10.1016\/j.neucom.2026.134494_bib0010","doi-asserted-by":"crossref","first-page":"7353","DOI":"10.1073\/pnas.1510489113","article-title":"Recursive partitioning for heterogeneous causal effects","volume":"113","author":"Athey","year":"2016","journal-title":"Proc. Natl. Acad. Sci."},{"key":"10.1016\/j.neucom.2026.134494_bib0015","series-title":"International Conference on Artificial Intelligence and Statistics","first-page":"1810","article-title":"Nonparametric estimation of heterogeneous treatment effects: from theory to learning algorithms","author":"Curth","year":"2021"},{"key":"10.1016\/j.neucom.2026.134494_bib0020","first-page":"15883","article-title":"On inductive biases for heterogeneous treatment effect estimation","volume":"34","author":"Curth","year":"2021","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.neucom.2026.134494_bib0025","author":"Diemert"},{"key":"10.1016\/j.neucom.2026.134494_bib0030","series-title":"Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)","first-page":"845","article-title":"Low resource dependency parsing: cross-lingual parameter sharing in a neural network parser","author":"Duong","year":"2015"},{"key":"10.1016\/j.neucom.2026.134494_bib0035","author":"Eigen"},{"key":"10.1016\/j.neucom.2026.134494_bib0040","series-title":"Machine Learning for Marketing Decision Support","article-title":"Revenue uplift modeling","author":"Gubela","year":"2017"},{"key":"10.1016\/j.neucom.2026.134494_bib0045","series-title":"International Conference on Machine Learning","first-page":"3854","article-title":"Learning to branch for multi-task learning","author":"Guo","year":"2020"},{"key":"10.1016\/j.neucom.2026.134494_bib0050","series-title":"International Conference on Predictive Applications and APIs","first-page":"1","article-title":"Causal inference and uplift modelling: a review of the literature","author":"Gutierrez","year":"2017"},{"key":"10.1016\/j.neucom.2026.134494_bib0055","series-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition","first-page":"770","article-title":"Deep residual learning for image recognition","author":"He","year":"2016"},{"key":"10.1016\/j.neucom.2026.134494_bib0060","series-title":"Companion Proceedings of the ACM on Web Conference 2024","first-page":"226","article-title":"Entire chain uplift modeling with context-enhanced learning for intelligent marketing","author":"Huang","year":"2024"},{"key":"10.1016\/j.neucom.2026.134494_bib0065","series-title":"2021 IEEE International Conference on Data Mining (ICDM)","first-page":"1156","article-title":"Addressing exposure bias in uplift modeling for large-scale online advertising","author":"Ke","year":"2021"},{"key":"10.1016\/j.neucom.2026.134494_bib0070","doi-asserted-by":"crossref","first-page":"3008","DOI":"10.1214\/23-EJS2157","article-title":"Towards optimal doubly robust estimation of heterogeneous causal effects","volume":"17","author":"Kennedy","year":"2023","journal-title":"Electron. J. Stat."},{"key":"10.1016\/j.neucom.2026.134494_bib0075","author":"Kingma"},{"key":"10.1016\/j.neucom.2026.134494_bib0080","doi-asserted-by":"crossref","first-page":"4156","DOI":"10.1073\/pnas.1804597116","article-title":"Metalearners for estimating heterogeneous treatment effects using machine learning","volume":"116","author":"K\u00fcnzel","year":"2019","journal-title":"Proc. Natl. Acad. Sci."},{"key":"10.1016\/j.neucom.2026.134494_bib0085","doi-asserted-by":"crossref","first-page":"54614","DOI":"10.52202\/075280-2380","article-title":"Removing hidden confounding in recommendation: a unified multi-task learning approach","volume":"36","author":"Li","year":"2023","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.neucom.2026.134494_bib0090","series-title":"Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","first-page":"1235","article-title":"Who should be given incentives? Counterfactual optimal treatment regimes learning for recommendation","author":"Li","year":"2023"},{"key":"10.1016\/j.neucom.2026.134494_bib0095","series-title":"Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","first-page":"4507","article-title":"Explicit feature interaction-aware uplift network for online marketing","author":"Liu","year":"2023"},{"key":"10.1016\/j.neucom.2026.134494_bib0100","doi-asserted-by":"crossref","first-page":"78","DOI":"10.1145\/772862.772872","article-title":"The true lift model: a novel data mining approach to response modeling in database marketing","volume":"4","author":"Lo","year":"2002","journal-title":"ACM SIGKDD Explor. Newsl."},{"key":"10.1016\/j.neucom.2026.134494_bib0105","author":"Loshchilov"},{"key":"10.1016\/j.neucom.2026.134494_bib0110","series-title":"Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining","first-page":"1930","article-title":"Modeling task relationships in multi-task learning with multi-gate mixture-of-experts","author":"Ma","year":"2018"},{"key":"10.1016\/j.neucom.2026.134494_bib0115","series-title":"Proceedings of the 31st ACM International Conference on Information & Knowledge Management","first-page":"3381","article-title":"Memento: neural model for estimating individual treatment effects for multiple treatments","author":"Mondal","year":"2022"},{"key":"10.1016\/j.neucom.2026.134494_bib0120","doi-asserted-by":"crossref","first-page":"299","DOI":"10.1093\/biomet\/asaa076","article-title":"Quasi-Oracle estimation of heterogeneous treatment effects","volume":"108","author":"Nie","year":"2021","journal-title":"Biometrika"},{"key":"10.1016\/j.neucom.2026.134494_bib0125","series-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","first-page":"10684","article-title":"High-resolution image synthesis with latent diffusion models","author":"Rombach","year":"2022"},{"key":"10.1016\/j.neucom.2026.134494_bib0130","doi-asserted-by":"crossref","first-page":"322","DOI":"10.1198\/016214504000001880","article-title":"Causal inference using potential outcomes: design, modeling, decisions","volume":"100","author":"Rubin","year":"2005","journal-title":"J. Am. Stat. Assoc."},{"key":"10.1016\/j.neucom.2026.134494_bib0135","author":"Ruder"},{"key":"10.1016\/j.neucom.2026.134494_bib0140","series-title":"Proceedings of the AAAI Conference on Artificial Intelligence","first-page":"5612","article-title":"Learning counterfactual representations for estimating individual dose-response curves","author":"Schwab","year":"2020"},{"key":"10.1016\/j.neucom.2026.134494_bib0145","series-title":"International Conference on Machine Learning","first-page":"3076","article-title":"Estimating individual treatment effect: generalization bounds and algorithms","author":"Shalit","year":"2017"},{"key":"10.1016\/j.neucom.2026.134494_bib0150","article-title":"Adapting neural networks for the estimation of treatment effects","volume":"32","author":"Shi","year":"2019","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.neucom.2026.134494_bib0155","series-title":"ICASSP 2024-2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","first-page":"5065","article-title":"M 3 TN: multi-gate mixture-of-experts based multi-valued treatment network for uplift modeling","author":"Sun","year":"2024"},{"key":"10.1016\/j.neucom.2026.134494_bib0160","series-title":"Artificial Intelligence Research and Development","first-page":"16","article-title":"Hydranet: a neural network for the estimation of multi-valued treatment effects","author":"Velasco-Regulez","year":"2023"},{"key":"10.1016\/j.neucom.2026.134494_bib0165","doi-asserted-by":"crossref","first-page":"1228","DOI":"10.1080\/01621459.2017.1319839","article-title":"Estimation and inference of heterogeneous treatment effects using random forests","volume":"113","author":"Wager","year":"2018","journal-title":"J. Am. Stat. Assoc."},{"key":"10.1016\/j.neucom.2026.134494_bib0170","doi-asserted-by":"crossref","first-page":"1755","DOI":"10.1109\/TIFS.2024.3516584","article-title":"Entire space counterfactual learning for reliable content recommendations","volume":"20","author":"Wang","year":"2024","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"key":"10.1016\/j.neucom.2026.134494_bib0175","doi-asserted-by":"crossref","first-page":"5404","DOI":"10.52202\/075280-0237","article-title":"Optimal transport for treatment effect estimation","volume":"36","author":"Wang","year":"2023","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.neucom.2026.134494_bib0180","author":"Wang"},{"key":"10.1016\/j.neucom.2026.134494_bib0185","series-title":"Pacific-Asia Conference on Knowledge Discovery and Data Mining","first-page":"200","article-title":"Learning discriminative representation base on attention for uplift","author":"Xu","year":"2022"},{"key":"10.1016\/j.neucom.2026.134494_bib0190","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3444944","article-title":"A survey on causal inference","volume":"15","author":"Yao","year":"2021","journal-title":"ACM Trans. Knowl. Discov. Data (TKDD)"},{"key":"10.1016\/j.neucom.2026.134494_bib0195","author":"Zhang"},{"key":"10.1016\/j.neucom.2026.134494_bib0200","first-page":"1","article-title":"A unified survey of treatment effect heterogeneity modelling and uplift modelling","volume":"54","author":"Zhang","year":"2021","journal-title":"ACM Comput. Surv. (CSUR)"},{"key":"10.1016\/j.neucom.2026.134494_bib0205","series-title":"Proceedings of the ACM Web Conference 2024","first-page":"3287","article-title":"Adversarial-enhanced causal multi-task framework for debiasing post-click conversion rate estimation","author":"Zhang","year":"2024"},{"key":"10.1016\/j.neucom.2026.134494_bib0210","series-title":"2017 IEEE International Conference on Data Mining (ICDM)","first-page":"1171","article-title":"A practically competitive and provably consistent algorithm for uplift modeling","author":"Zhao","year":"2017"},{"key":"10.1016\/j.neucom.2026.134494_bib0215","series-title":"Proceedings of the 2017 SIAM International Conference on Data Mining","first-page":"588","article-title":"Uplift modeling with multiple treatments and general response types","author":"Zhao","year":"2017"},{"key":"10.1016\/j.neucom.2026.134494_bib0220","series-title":"2019 IEEE International Conference on Data Science and Advanced Analytics (DSAA)","first-page":"422","article-title":"Uplift modeling for multiple treatments with cost optimization","author":"Zhao","year":"2019"},{"key":"10.1016\/j.neucom.2026.134494_bib0225","series-title":"Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","first-page":"4612","article-title":"DESCN: deep entire space cross networks for individual treatment effect estimation","author":"Zhong","year":"2022"},{"key":"10.1016\/j.neucom.2026.134494_bib0230","author":"Zhou"},{"key":"10.1016\/j.neucom.2026.134494_bib0235","series-title":"Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","first-page":"6368","article-title":"Decision focused causal learning for direct counterfactual marketing optimization","author":"Zhou","year":"2024"},{"key":"10.1016\/j.neucom.2026.134494_bib0240","series-title":"Proceedings of the AAAI Conference on Artificial Intelligence","first-page":"17175","article-title":"Contrastive balancing representation learning for heterogeneous dose-response curves estimation","author":"Zhu","year":"2024"}],"container-title":["Neurocomputing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0925231226018928?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0925231226018928?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,7,17]],"date-time":"2026-07-17T17:32:41Z","timestamp":1784309561000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0925231226018928"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,11]]},"references-count":48,"alternative-id":["S0925231226018928"],"URL":"https:\/\/doi.org\/10.1016\/j.neucom.2026.134494","relation":{},"ISSN":["0925-2312"],"issn-type":[{"value":"0925-2312","type":"print"}],"subject":[],"published":{"date-parts":[[2026,11]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"MTMT: Tiered treatment effect decomposition for multi-task uplift modeling","name":"articletitle","label":"Article Title"},{"value":"Neurocomputing","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.neucom.2026.134494","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 The Authors. Published by Elsevier B.V.","name":"copyright","label":"Copyright"}],"article-number":"134494"}}