{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,26]],"date-time":"2026-02-26T05:05:09Z","timestamp":1772082309537,"version":"3.50.1"},"publisher-location":"New York, NY, USA","reference-count":23,"publisher":"ACM","license":[{"start":{"date-parts":[[2022,10,17]],"date-time":"2022-10-17T00:00:00Z","timestamp":1665964800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2022,10,17]]},"DOI":"10.1145\/3511808.3557125","type":"proceedings-article","created":{"date-parts":[[2022,10,16]],"date-time":"2022-10-16T01:29:57Z","timestamp":1665883797000},"page":"3381-3390","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":6,"title":["MEMENTO: Neural Model for Estimating Individual Treatment Effects for Multiple Treatments"],"prefix":"10.1145","author":[{"given":"Abhirup","family":"Mondal","sequence":"first","affiliation":[{"name":"Amazon, Bengaluru, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Anirban","family":"Majumder","sequence":"additional","affiliation":[{"name":"Amazon, Bengaluru, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Vineet","family":"Chaoji","sequence":"additional","affiliation":[{"name":"Amazon, Bengaluru, India"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2022,10,17]]},"reference":[{"key":"e_1_3_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1080\/00273171.2011.568786"},{"key":"e_1_3_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1093\/aje\/kwq439"},{"key":"e_1_3_2_1_3_1","unstructured":"Yarin Gal and Zoubin Ghahramani. 2016. Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning. arxiv: 1506.02142 [stat.ML]  Yarin Gal and Zoubin Ghahramani. 2016. Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning. arxiv: 1506.02142 [stat.ML]"},{"key":"e_1_3_2_1_4_1","first-page":"723","article-title":"A Kernel Two-Sample Test","volume":"13","author":"Gretton Arthur","year":"2012","unstructured":"Arthur Gretton , Karsten M. Borgwardt , Malte J. Rasch , Bernhard Sch\u00f6lkopf , and Alexander Smola . 2012 . A Kernel Two-Sample Test . J. Mach. Learn. Res. , Vol. 13 (2012), 723 -- 773 . Arthur Gretton, Karsten M. Borgwardt, Malte J. Rasch, Bernhard Sch\u00f6lkopf, and Alexander Smola. 2012. A Kernel Two-Sample Test. J. Mach. Learn. Res., Vol. 13 (2012), 723--773.","journal-title":"J. Mach. Learn. Res."},{"key":"e_1_3_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1198\/jcgs.2010.08162"},{"key":"e_1_3_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1198\/016214504000001187"},{"key":"e_1_3_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1093\/biomet\/87.3.706"},{"key":"e_1_3_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1162\/003465304323023651"},{"key":"e_1_3_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1214\/19-AOAS1282"},{"key":"e_1_3_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1002\/sim.6768"},{"key":"e_1_3_2_1_12_1","first-page":"3","article-title":"Estimation of Causal Effects with Multiple Treatments","volume":"32","author":"Lopez Michael J.","year":"2017","unstructured":"Michael J. Lopez and Roee Gutman . 2017 . Estimation of Causal Effects with Multiple Treatments : A Review and New Ideas. Statist. Sci. , Vol. 32 , 3 (Aug 2017), 432--454. https:\/\/doi.org\/10.1214\/17-sts612 10.1214\/17-sts612 Michael J. Lopez and Roee Gutman. 2017. Estimation of Causal Effects with Multiple Treatments: A Review and New Ideas. Statist. Sci., Vol. 32, 3 (Aug 2017), 432--454. https:\/\/doi.org\/10.1214\/17-sts612","journal-title":"A Review and New Ideas. Statist. Sci."},{"key":"e_1_3_2_1_13_1","volume-title":"Cost-Effective Incentive Allocation via Structured Counterfactual Inference. arxiv","author":"Lopez Romain","year":"1902","unstructured":"Romain Lopez , Chenchen Li , Xiang Yan , Junwu Xiong , Michael I. Jordan , Yuan Qi , and Le Song . 2019. Cost-Effective Incentive Allocation via Structured Counterfactual Inference. arxiv : 1902 .02495 [stat.ML] Romain Lopez, Chenchen Li, Xiang Yan, Junwu Xiong, Michael I. Jordan, Yuan Qi, and Le Song. 2019. Cost-Effective Incentive Allocation via Structured Counterfactual Inference. arxiv: 1902.02495 [stat.ML]"},{"key":"e_1_3_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1002\/sim.5753"},{"key":"e_1_3_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1017\/CBO9780511803161"},{"key":"e_1_3_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1093\/biomet\/70.1.41"},{"key":"e_1_3_2_1_17_1","volume-title":"Multiple Treatment Effect Estimation Using Deep Generative Model with Task Embedding. In The World Wide Web Conference","author":"Saini Shiv Kumar","year":"2019","unstructured":"Shiv Kumar Saini , Sunny Dhamnani , Aakash, Akil Arif Ibrahim , and Prithviraj Chavan . 2019 . Multiple Treatment Effect Estimation Using Deep Generative Model with Task Embedding. In The World Wide Web Conference ( San Francisco, CA, USA) (WWW '19). Association for Computing Machinery, New York, NY, USA, 1601--1611. Shiv Kumar Saini, Sunny Dhamnani, Aakash, Akil Arif Ibrahim, and Prithviraj Chavan. 2019. Multiple Treatment Effect Estimation Using Deep Generative Model with Task Embedding. In The World Wide Web Conference (San Francisco, CA, USA) (WWW '19). Association for Computing Machinery, New York, NY, USA, 1601--1611."},{"key":"e_1_3_2_1_18_1","volume-title":"Learning Counterfactual Representations for Estimating Individual Dose-Response Curves. In The Thirty-Fourth AAAI Conference on Artificial Intelligence. AAAI Press, 5612--5619","author":"Schwab Patrick","year":"2020","unstructured":"Patrick Schwab , Lorenz Linhardt , Stefan Bauer , Joachim M. Buhmann , and Walter Karlen . 2020 . Learning Counterfactual Representations for Estimating Individual Dose-Response Curves. In The Thirty-Fourth AAAI Conference on Artificial Intelligence. AAAI Press, 5612--5619 . Patrick Schwab, Lorenz Linhardt, Stefan Bauer, Joachim M. Buhmann, and Walter Karlen. 2020. Learning Counterfactual Representations for Estimating Individual Dose-Response Curves. In The Thirty-Fourth AAAI Conference on Artificial Intelligence. AAAI Press, 5612--5619."},{"key":"e_1_3_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1002\/sim.8147"},{"key":"e_1_3_2_1_20_1","volume-title":"Proceedings of the 34th International Conference on Machine Learning (Proceedings of Machine Learning Research","volume":"3085","author":"Shalit Uri","year":"2017","unstructured":"Uri Shalit , Fredrik D. Johansson , and David Sontag . 2017 . Estimating individual treatment effect: generalization bounds and algorithms . In Proceedings of the 34th International Conference on Machine Learning (Proceedings of Machine Learning Research , Vol. 70), Doina Precup and Yee Whye Teh (Eds.). PMLR, International Convention Centre, Sydney, Australia, 3076-- 3085 . Uri Shalit, Fredrik D. Johansson, and David Sontag. 2017. Estimating individual treatment effect: generalization bounds and algorithms. In Proceedings of the 34th International Conference on Machine Learning (Proceedings of Machine Learning Research, Vol. 70), Doina Precup and Yee Whye Teh (Eds.). PMLR, International Convention Centre, Sydney, Australia, 3076--3085."},{"key":"e_1_3_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.1214\/12-EJS722"},{"key":"e_1_3_2_1_22_1","volume-title":"Lanckriet","author":"Sriperumbudur Bharath K.","year":"2009","unstructured":"Bharath K. Sriperumbudur , Kenji Fukumizu , Arthur Gretton , Bernhard Sch\u00f6lkopf , and Gert R. G . Lanckriet . 2009 . On integral probability metrics, \u03c6-divergences and binary classification. arxiv: 0901.2698 [cs.IT] Bharath K. Sriperumbudur, Kenji Fukumizu, Arthur Gretton, Bernhard Sch\u00f6lkopf, and Gert R. G. Lanckriet. 2009. On integral probability metrics, \u03c6-divergences and binary classification. arxiv: 0901.2698 [cs.IT]"},{"key":"e_1_3_2_1_23_1","volume-title":"Advances in Neural Information Processing Systems","author":"Yao Liuyi","unstructured":"Liuyi Yao , Sheng Li , Yaliang Li , Mengdi Huai , Jing Gao , and Aidong Zhang . 2018. Representation Learning for Treatment Effect Estimation from Observational Data . In Advances in Neural Information Processing Systems , S. Bengio, H. Wallach, H. Larochelle, K. Grauman, N. Cesa-Bianchi, and R. Garnett (Eds.), Vol. 31 . Curran Associates, Inc. , 2633--2643. Liuyi Yao, Sheng Li, Yaliang Li, Mengdi Huai, Jing Gao, and Aidong Zhang. 2018. Representation Learning for Treatment Effect Estimation from Observational Data. In Advances in Neural Information Processing Systems, S. Bengio, H. Wallach, H. Larochelle, K. Grauman, N. Cesa-Bianchi, and R. Garnett (Eds.), Vol. 31. Curran Associates, Inc., 2633--2643."},{"key":"e_1_3_2_1_24_1","volume-title":"International Conference on Learning Representations.","author":"Yoon Jinsung","unstructured":"Jinsung Yoon , James Jordon , and Mihaela van der Schaar. 2018. GANITE: Estimation of Individualized Treatment Effects using Generative Adversarial Nets . In International Conference on Learning Representations. Jinsung Yoon, James Jordon, and Mihaela van der Schaar. 2018. GANITE: Estimation of Individualized Treatment Effects using Generative Adversarial Nets. In International Conference on Learning Representations."}],"event":{"name":"CIKM '22: The 31st ACM International Conference on Information and Knowledge Management","location":"Atlanta GA USA","acronym":"CIKM '22","sponsor":["SIGWEB ACM Special Interest Group on Hypertext, Hypermedia, and Web","SIGIR ACM Special Interest Group on Information Retrieval"]},"container-title":["Proceedings of the 31st ACM International Conference on Information & Knowledge Management"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3511808.3557125","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3511808.3557125","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T19:30:56Z","timestamp":1750188656000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3511808.3557125"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,10,17]]},"references-count":23,"alternative-id":["10.1145\/3511808.3557125","10.1145\/3511808"],"URL":"https:\/\/doi.org\/10.1145\/3511808.3557125","relation":{},"subject":[],"published":{"date-parts":[[2022,10,17]]},"assertion":[{"value":"2022-10-17","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}