{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,31]],"date-time":"2026-01-31T00:46:10Z","timestamp":1769820370247,"version":"3.49.0"},"reference-count":24,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,12,17]],"date-time":"2025-12-17T00:00:00Z","timestamp":1765929600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,12,17]],"date-time":"2025-12-17T00:00:00Z","timestamp":1765929600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100018625","name":"Science and Technology Innovation Plan Of Shanghai Science and Technology Commission","doi-asserted-by":"publisher","award":["23JS1400500"],"award-info":[{"award-number":["23JS1400500"]}],"id":[{"id":"10.13039\/501100018625","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100018625","name":"Science and Technology Innovation Plan Of Shanghai Science and Technology Commission","doi-asserted-by":"publisher","award":["23JS1400800"],"award-info":[{"award-number":["23JS1400800"]}],"id":[{"id":"10.13039\/501100018625","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"Natural Science Foundation of China","doi-asserted-by":"crossref","award":["72331005"],"award-info":[{"award-number":["72331005"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100013139","name":"Humanities and Social Science Fund of Ministry of Education of China","doi-asserted-by":"publisher","award":["23YJC910006"],"award-info":[{"award-number":["23YJC910006"]}],"id":[{"id":"10.13039\/501100013139","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Comput Stat"],"published-print":{"date-parts":[[2026,1]]},"DOI":"10.1007\/s00180-025-01705-3","type":"journal-article","created":{"date-parts":[[2025,12,17]],"date-time":"2025-12-17T09:12:04Z","timestamp":1765962724000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Individualized treatment effect estimation with compromised adversarial nets"],"prefix":"10.1007","volume":"41","author":[{"given":"Atomsa Gemechu","family":"Abdisa","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1217-6989","authenticated-orcid":false,"given":"Yingchun","family":"Zhou","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuqi","family":"Qiu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,12,17]]},"reference":[{"issue":"2","key":"1705_CR1","doi-asserted-by":"publisher","first-page":"1148","DOI":"10.1214\/18-AOS1709","volume":"47","author":"S Athey","year":"2019","unstructured":"Athey S, Tibshirani J, Wager S (2019) Generalized random forests. Ann Stat 47(2):1148\u20131178. https:\/\/doi.org\/10.1214\/18-AOS1709","journal-title":"Ann Stat"},{"issue":"1","key":"1705_CR2","doi-asserted-by":"publisher","first-page":"266","DOI":"10.1214\/09-AOAS285","volume":"4","author":"HA Chipman","year":"2010","unstructured":"Chipman HA, George EI, McCulloch RE (2010) BART: Bayesian additive regression trees. Ann Appl Stat 4(1):266\u2013298. https:\/\/doi.org\/10.1214\/09-AOAS285","journal-title":"Ann Appl Stat"},{"issue":"4","key":"1705_CR3","doi-asserted-by":"publisher","first-page":"710","DOI":"10.1002\/cpt.3159","volume":"115","author":"A Curth","year":"2024","unstructured":"Curth A, Peck RW, McKinney E, Weatherall J, Der Schaar M (2024) Using machine learning to individualize treatment effect estimation: challenges and opportunities. Clin Pharmacol Ther 115(4):710\u2013719","journal-title":"Clin Pharmacol Ther"},{"issue":"1","key":"1705_CR4","doi-asserted-by":"publisher","first-page":"43","DOI":"10.1214\/18-STS667","volume":"34","author":"V Dorie","year":"2019","unstructured":"Dorie V, Hill J, Shalit U, Scott M, Cervone D (2019) Automated versus do-it-yourself methods for causal inference: lessons learned from a data analysis competition. Stat Sci 34(1):43\u201368","journal-title":"Stat Sci"},{"key":"1705_CR5","doi-asserted-by":"publisher","first-page":"585804","DOI":"10.3389\/fgene.2020.585804","volume":"11","author":"Q Ge","year":"2020","unstructured":"Ge Q, Huang X, Fang S, Guo S, Liu Y, Lin W, Xiong M (2020) Conditional generative adversarial networks for individualized treatment effect estimation and treatment selection. Front Genet 11:585804","journal-title":"Front Genet"},{"key":"1705_CR6","unstructured":"Goodfellow I, Bengio Y, Courville A (2016) Deep Learning. MIT Press. http:\/\/www.deeplearningbook.org"},{"key":"1705_CR7","unstructured":"Goodfellow I, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S, Courville A, Bengio Y (2014) Generative adversarial nets. Advances in Neural Information Processing Systems 27"},{"issue":"1","key":"1705_CR8","doi-asserted-by":"publisher","first-page":"217","DOI":"10.1198\/jcgs.2010.08162","volume":"20","author":"JL Hill","year":"2011","unstructured":"Hill JL (2011) Bayesian nonparametric modeling for causal inference. J Comput Graph Stat 20(1):217\u2013240. https:\/\/doi.org\/10.1198\/jcgs.2010.08162","journal-title":"J Comput Graph Stat"},{"issue":"396","key":"1705_CR9","doi-asserted-by":"publisher","first-page":"945","DOI":"10.1080\/01621459.1986.10478354","volume":"81","author":"PW Holland","year":"1986","unstructured":"Holland PW (1986) Statistics and causal inference. J Am Stat Assoc 81(396):945\u2013960","journal-title":"J Am Stat Assoc"},{"key":"1705_CR10","doi-asserted-by":"publisher","unstructured":"Kent DM, Steyerberg E, Klaveren D (2018) Personalized evidence based medicine: predictive approaches to heterogeneous treatment effects. BMJ 363 https:\/\/doi.org\/10.1136\/bmj.k4245https:\/\/www.bmj.com\/content\/363\/bmj.k4245.full.pdf","DOI":"10.1136\/bmj.k4245"},{"issue":"5","key":"1705_CR11","doi-asserted-by":"publisher","first-page":"911","DOI":"10.1111\/rssb.12445","volume":"83","author":"L Lei","year":"2021","unstructured":"Lei L, Cand\u00e8s EJ (2021) Conformal inference of counterfactuals and individual treatment effects. J R Stat Soc Ser B Stat Methodol 83(5):911\u2013938","journal-title":"J R Stat Soc Ser B Stat Methodol"},{"key":"1705_CR12","doi-asserted-by":"crossref","unstructured":"Mao X, Li Q, Xie H, Lau RY, Wang Z, Paul\u00a0Smolley S (2017) Least squares generative adversarial networks. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2794\u20132802","DOI":"10.1109\/ICCV.2017.304"},{"key":"1705_CR13","unstructured":"Neyman J (1923) On the application of probability theory to agricultural experiments. essay on principles. Ann Agric Sci, 1\u201351"},{"issue":"387","key":"1705_CR14","doi-asserted-by":"publisher","first-page":"516","DOI":"10.1080\/01621459.1984.10478078","volume":"79","author":"PR Rosenbaum","year":"1984","unstructured":"Rosenbaum PR, Rubin DB (1984) Reducing bias in observational studies using subclassification on the propensity score. J Am Stat Assoc 79(387):516\u2013524","journal-title":"J Am Stat Assoc"},{"key":"1705_CR15","unstructured":"Rubin DB (1975) Bayesian inference for causality: The importance of randomization. In: In Proceedings of Social Statistics Section of Am Stat. Assoc., pp. 233\u2013239. American Statistical Association Alexandria, VA"},{"issue":"5","key":"1705_CR16","doi-asserted-by":"publisher","first-page":"688","DOI":"10.1037\/h0037350","volume":"66","author":"DB Rubin","year":"1974","unstructured":"Rubin DB (1974) Estimating causal effects of treatments in randomized and nonrandomized studies. J Educ Psychol 66(5):688","journal-title":"J Educ Psychol"},{"issue":"1","key":"1705_CR17","doi-asserted-by":"publisher","first-page":"34","DOI":"10.1214\/aos\/1176344064","volume":"6","author":"DB Rubin","year":"1978","unstructured":"Rubin DB (1978) Bayesian inference for causal effects: the role of randomization. Ann Stat 6(1):34\u201358. https:\/\/doi.org\/10.1214\/aos\/1176344064","journal-title":"Ann Stat"},{"issue":"469","key":"1705_CR18","doi-asserted-by":"publisher","first-page":"322","DOI":"10.1198\/016214504000001880","volume":"100","author":"DB Rubin","year":"2005","unstructured":"Rubin DB (2005) Causal inference using potential outcomes: design, modeling, decisions. J Am Stat Assoc 100(469):322\u2013331","journal-title":"J Am Stat Assoc"},{"key":"1705_CR19","unstructured":"Shalit U, Johansson FD, Sontag D (2017) Estimating individual treatment effect: generalization bounds and algorithms. In: International Conference on Machine Learning, pp. 3076\u20133085. PMLR"},{"key":"1705_CR20","unstructured":"Shi C, Blei D, Veitch V (2019) Adapting neural networks for the estimation of treatment effects. Advances in Neural Information Processing Systems 32"},{"issue":"1\u20132","key":"1705_CR21","first-page":"305","volume":"125","author":"JA Smith","year":"2005","unstructured":"Smith JA, Todd PE (2005) Does matching overcome lalonde\u2019s critique of nonexperimental estimators? J Econ 125(1\u20132):305\u2013353","journal-title":"J Econ"},{"issue":"523","key":"1705_CR22","doi-asserted-by":"publisher","first-page":"1228","DOI":"10.1080\/01621459.2017.1319839","volume":"113","author":"S Wager","year":"2018","unstructured":"Wager S, Athey S (2018) Estimation and inference of heterogeneous treatment effects using random forests. J Am Stat Assoc 113(523):1228\u20131242","journal-title":"J Am Stat Assoc"},{"issue":"3","key":"1705_CR23","doi-asserted-by":"publisher","first-page":"702","DOI":"10.1093\/biomet\/78.3.702","volume":"78","author":"YG Yatracos","year":"1991","unstructured":"Yatracos YG (1991) A note on tukey\u2019s polyefficiency. Biometrika 78(3):702\u2013703","journal-title":"Biometrika"},{"key":"1705_CR24","unstructured":"Yoon J, Jordon J, Van Der\u00a0Schaar M (2018) Ganite: Estimation of individualized treatment effects using generative adversarial nets. In: International Conference on Learning Representations"}],"container-title":["Computational Statistics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00180-025-01705-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00180-025-01705-3","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00180-025-01705-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,30]],"date-time":"2026-01-30T12:14:02Z","timestamp":1769775242000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00180-025-01705-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,12,17]]},"references-count":24,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2026,1]]}},"alternative-id":["1705"],"URL":"https:\/\/doi.org\/10.1007\/s00180-025-01705-3","relation":{},"ISSN":["0943-4062","1613-9658"],"issn-type":[{"value":"0943-4062","type":"print"},{"value":"1613-9658","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,12,17]]},"assertion":[{"value":"15 March 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"5 September 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"17 December 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"There is no author(s) reported potential Conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}],"article-number":"2"}}