{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,9]],"date-time":"2026-03-09T06:03:33Z","timestamp":1773036213655,"version":"3.50.1"},"reference-count":21,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T00:00:00Z","timestamp":1777593600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T00:00:00Z","timestamp":1777593600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T00:00:00Z","timestamp":1777593600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T00:00:00Z","timestamp":1777593600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T00:00:00Z","timestamp":1777593600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T00:00:00Z","timestamp":1777593600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T00:00:00Z","timestamp":1777593600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100003951","name":"Orange SA","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100003951","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100015519","name":"University of Caen Normandy","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100015519","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Data &amp; Knowledge Engineering"],"published-print":{"date-parts":[[2026,5]]},"DOI":"10.1016\/j.datak.2026.102565","type":"journal-article","created":{"date-parts":[[2026,1,24]],"date-time":"2026-01-24T16:10:54Z","timestamp":1769271054000},"page":"102565","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["Multi-treatment uplift evaluation on non-random assignment biased data"],"prefix":"10.1016","volume":"163","author":[{"given":"Nathan","family":"Le Boudec","sequence":"first","affiliation":[]},{"given":"Nicolas","family":"Voisine","sequence":"additional","affiliation":[]},{"given":"Bruno","family":"Cr\u00e9milleux","sequence":"additional","affiliation":[]}],"member":"78","reference":[{"key":"10.1016\/j.datak.2026.102565_b1","series-title":"White Paper TR-2011-1","first-page":"1","article-title":"Real-world uplift modelling with significance-based uplift trees","author":"Radcliffe","year":"2011"},{"issue":"5","key":"10.1016\/j.datak.2026.102565_b2","doi-asserted-by":"crossref","first-page":"688","DOI":"10.1037\/h0037350","article-title":"Estimating causal effects of treatments in randomized and nonrandomized studies","volume":"66","author":"Rubin","year":"1974","journal-title":"J. Educ. Psychol."},{"key":"10.1016\/j.datak.2026.102565_b3","unstructured":"P. Gutierrez, J.Y. G\u00e9rardy, Causal Inference and Uplift Modeling: A review of the literature, in: JMLR: Workshop and Conference Proceedings, vol. 67, 2016, pp. 1\u201313."},{"key":"10.1016\/j.datak.2026.102565_b4","series-title":"Automated Machine Learning : Methods, Systems, Challenges","first-page":"35","article-title":"Meta-learning","author":"Vanschoren","year":"2019"},{"issue":"3\u20134","key":"10.1016\/j.datak.2026.102565_b5","doi-asserted-by":"crossref","first-page":"230","DOI":"10.1080\/01969722.2015.1012892","article-title":"Uplift random forests","volume":"46","author":"Guelman","year":"2015","journal-title":"Cybern. Syst."},{"issue":"3","key":"10.1016\/j.datak.2026.102565_b6","doi-asserted-by":"crossref","first-page":"209","DOI":"10.1007\/BF01072450","article-title":"When random assignment fails : Some lessons from the minneapolis spouse abuse experiment","volume":"4","author":"Berk","year":"1988","journal-title":"J. Quant. Criminol."},{"key":"10.1016\/j.datak.2026.102565_b7","doi-asserted-by":"crossref","unstructured":"M. Rafla, N. Voisine, B. Cr\u00e9milleux, Evaluation of uplift models with non-random assignment bias, in: 20th Int. Symposium on Intelligent Data Analysis, IDA 2022, Rennes, France, April 20-22, Springer, pp. 251\u2013263.","DOI":"10.1007\/978-3-031-01333-1_20"},{"issue":"8","key":"10.1016\/j.datak.2026.102565_b8","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."},{"key":"10.1016\/j.datak.2026.102565_b9","doi-asserted-by":"crossref","unstructured":"Y. Zhao, X. Fang, D. Simchi-Levi, Uplift modeling with multiple treatments and general response types, in: 2017 SIAM International Conference on Data Mining, 2017.","DOI":"10.1137\/1.9781611974973.66"},{"issue":"5","key":"10.1016\/j.datak.2026.102565_b10","first-page":"1","article-title":"Machine learning methods for estimating heterogeneous causal effects","volume":"1050","author":"Athey","year":"2015","journal-title":"Stat"},{"issue":"10","key":"10.1016\/j.datak.2026.102565_b11","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.datak.2026.102565_b12","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1002\/dir.10035","article-title":"Incremental value modeling","volume":"16","author":"Hansotia","year":"2002","journal-title":"J. Interact. Mark."},{"key":"10.1016\/j.datak.2026.102565_b13","doi-asserted-by":"crossref","first-page":"169","DOI":"10.1023\/A:1020363010465","article-title":"Using propensity scores to help design observational studies : Application to the tobacco litigation","volume":"2","author":"Rubin","year":"2001","journal-title":"Health Serv. Outcomes Res. Methodol."},{"issue":"2","key":"10.1016\/j.datak.2026.102565_b14","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":"2020","journal-title":"Biometrika"},{"key":"10.1016\/j.datak.2026.102565_b15","series-title":"Optimal doubly robust estimation of heterogeneous causal effects","author":"Kennedy","year":"2020"},{"issue":"2","key":"10.1016\/j.datak.2026.102565_b16","doi-asserted-by":"crossref","first-page":"303","DOI":"10.1007\/s10115-011-0434-0","article-title":"Decision trees for uplift modeling with single and multiple treatments","volume":"32","author":"Rzepakowski","year":"2012","journal-title":"Knowl. Inf. Syst."},{"key":"10.1016\/j.datak.2026.102565_b17","series-title":"Proceedings of the 34th International Conference on Machine Learning","first-page":"3076","article-title":"Estimating individual treatment effect: generalization bounds and algorithms","volume":"vol. 70","author":"Shalit","year":"2017"},{"key":"10.1016\/j.datak.2026.102565_b18","series-title":"Benchmarking for deep uplift modeling in online marketing","author":"Liu","year":"2024"},{"key":"10.1016\/j.datak.2026.102565_b19","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1007\/s10618-019-00670-y","article-title":"A survey and benchmarking study of multitreatment uplift modeling","volume":"34","author":"Olaya","year":"2020","journal-title":"Data Min. Knowl. Discov."},{"issue":"3","key":"10.1016\/j.datak.2026.102565_b20","doi-asserted-by":"crossref","first-page":"875","DOI":"10.1007\/s10796-022-10283-4","article-title":"Multiple treatment modeling for target marketing campaigns: A large-scale benchmark study","volume":"26","author":"Gubela","year":"2024","journal-title":"Inf. Syst. Front."},{"key":"10.1016\/j.datak.2026.102565_b21","series-title":"Enhancing Uplift Modeling in Multi-Treatment Marketing Campaigns: Leveraging Score Ranking and Calibration Techniques","author":"Park","year":"2024"}],"container-title":["Data &amp; Knowledge Engineering"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0169023X26000121?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0169023X26000121?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,3,9]],"date-time":"2026-03-09T05:04:50Z","timestamp":1773032690000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0169023X26000121"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,5]]},"references-count":21,"alternative-id":["S0169023X26000121"],"URL":"https:\/\/doi.org\/10.1016\/j.datak.2026.102565","relation":{},"ISSN":["0169-023X"],"issn-type":[{"value":"0169-023X","type":"print"}],"subject":[],"published":{"date-parts":[[2026,5]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Multi-treatment uplift evaluation on non-random assignment biased data","name":"articletitle","label":"Article Title"},{"value":"Data & Knowledge Engineering","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.datak.2026.102565","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"102565"}}