{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,8]],"date-time":"2025-09-08T06:36:21Z","timestamp":1757313381851,"version":"3.44.0"},"publisher-location":"New York, NY, USA","reference-count":30,"publisher":"ACM","license":[{"start":{"date-parts":[[2024,5,13]],"date-time":"2024-05-13T00:00:00Z","timestamp":1715558400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/501100006374","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62176028"],"award-info":[{"award-number":["62176028"]}],"id":[{"id":"10.13039\/501100006374","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Science and Technology Research Program of Chongqing Municipal Education Commission","award":["KJZD-K202204402 and KJZD-K202304401"],"award-info":[{"award-number":["KJZD-K202204402 and KJZD-K202304401"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2024,5,13]]},"DOI":"10.1145\/3589335.3648320","type":"proceedings-article","created":{"date-parts":[[2024,5,12]],"date-time":"2024-05-12T18:41:21Z","timestamp":1715539281000},"page":"226-234","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":2,"title":["Entire Chain Uplift Modeling with Context-Enhanced Learning for Intelligent Marketing"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-2044-1625","authenticated-orcid":false,"given":"Yinqiu","family":"Huang","sequence":"first","affiliation":[{"name":"Chongqing University, Chongqing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8349-3626","authenticated-orcid":false,"given":"Shuli","family":"Wang","sequence":"additional","affiliation":[{"name":"Meituan, Chengdu, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0127-7477","authenticated-orcid":false,"given":"Min","family":"Gao","sequence":"additional","affiliation":[{"name":"Chongqing University, Chongqing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-8174-585X","authenticated-orcid":false,"given":"Xue","family":"Wei","sequence":"additional","affiliation":[{"name":"Meituan, Chengdu, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-2456-3621","authenticated-orcid":false,"given":"Changhao","family":"Li","sequence":"additional","affiliation":[{"name":"Meituan, Chengdu, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-3683-756X","authenticated-orcid":false,"given":"Chuan","family":"Luo","sequence":"additional","affiliation":[{"name":"Meituan, Chengdu, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-9135-7488","authenticated-orcid":false,"given":"Yinhua","family":"Zhu","sequence":"additional","affiliation":[{"name":"Meituan, Chengdu, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-9093-3053","authenticated-orcid":false,"given":"Xiong","family":"Xiao","sequence":"additional","affiliation":[{"name":"Meituan, Chengdu, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-5634-0729","authenticated-orcid":false,"given":"Yi","family":"Luo","sequence":"additional","affiliation":[{"name":"Meituan, Chengdu, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2024,5,13]]},"reference":[{"key":"e_1_3_2_2_1_1","volume-title":"Jane Leber Herr, and Guido W Imbens","author":"Abadie Alberto","year":"2004","unstructured":"Alberto Abadie, David Drukker, Jane Leber Herr, and Guido W Imbens. 2004. Implementing matching estimators for average treatment effects in Stata. The stata journal, Vol. 4, 3 (2004), 290--311."},{"key":"e_1_3_2_2_2_1","doi-asserted-by":"publisher","DOI":"10.1214\/18-AOS1709"},{"key":"e_1_3_2_2_3_1","first-page":"16434","article-title":"Estimating the effects of continuous-valued interventions using generative adversarial networks","volume":"33","author":"Bica Ioana","year":"2020","unstructured":"Ioana Bica, James Jordon, and Mihaela van der Schaar. 2020. Estimating the effects of continuous-valued interventions using generative adversarial networks. Advances in Neural Information Processing Systems , Vol. 33 (2020), 16434--16445.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_2_4_1","volume-title":"Automated Search for Resource-Efficient Branched Multi-Task Networks. In 31st British Machine Vision Conference 2020, BMVC 2020. BMVA Press, 359","author":"Br\u00fcggemann David","year":"2020","unstructured":"David Br\u00fcggemann, Menelaos Kanakis, Stamatios Georgoulis, and Luc Van Gool. 2020. Automated Search for Resource-Efficient Branched Multi-Task Networks. In 31st British Machine Vision Conference 2020, BMVC 2020. BMVA Press, 359."},{"key":"e_1_3_2_2_5_1","doi-asserted-by":"publisher","DOI":"10.1145\/3580305.3599884"},{"key":"e_1_3_2_2_6_1","volume-title":"Causalml: Python package for causal machine learning. arXiv preprint arXiv:2002.11631","author":"Chen Huigang","year":"2020","unstructured":"Huigang Chen, Totte Harinen, Jeong-Yoon Lee, Mike Yung, and Zhenyu Zhao. 2020. Causalml: Python package for causal machine learning. arXiv preprint arXiv:2002.11631 (2020)."},{"key":"e_1_3_2_2_7_1","volume-title":"A large scale benchmark for individual treatment effect prediction and uplift modeling. arXiv preprint arXiv:2111.10106","author":"Diemert Eustache","year":"2021","unstructured":"Eustache Diemert, Artem Betlei, Christophe Renaudin, Massih-Reza Amini, Th\u00e9ophane Gregoir, and Thibaud Rahier. 2021. A large scale benchmark for individual treatment effect prediction and uplift modeling. arXiv preprint arXiv:2111.10106 (2021)."},{"key":"e_1_3_2_2_8_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00332"},{"key":"e_1_3_2_2_9_1","volume-title":"International conference on machine learning. PMLR, 3854--3863","author":"Guo Pengsheng","year":"2020","unstructured":"Pengsheng Guo, Chen-Yu Lee, and Daniel Ulbricht. 2020. Learning to branch for multi-task learning. In International conference on machine learning. PMLR, 3854--3863."},{"volume-title":"Causal inference in statistics, social, and biomedical sciences","author":"Imbens Guido W","key":"e_1_3_2_2_10_1","unstructured":"Guido W Imbens and Donald B Rubin. 2015. Causal inference in statistics, social, and biomedical sciences. Cambridge University Press."},{"key":"e_1_3_2_2_11_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM51629.2021.00138"},{"key":"e_1_3_2_2_12_1","doi-asserted-by":"publisher","DOI":"10.1145\/3097983.3098032"},{"key":"e_1_3_2_2_13_1","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.1804597116"},{"key":"e_1_3_2_2_14_1","doi-asserted-by":"publisher","DOI":"10.1145\/3038912.3052616"},{"key":"e_1_3_2_2_15_1","volume-title":"Explicit Feature Interaction-aware Uplift Network for Online Marketing. arXiv preprint arXiv:2306.00315","author":"Liu Dugang","year":"2023","unstructured":"Dugang Liu, Xing Tang, Han Gao, Fuyuan Lyu, and Xiuqiang He. 2023. Explicit Feature Interaction-aware Uplift Network for Online Marketing. arXiv preprint arXiv:2306.00315 (2023)."},{"key":"e_1_3_2_2_16_1","volume-title":"Causal effect inference with deep latent-variable models. Advances in neural information processing systems","author":"Louizos Christos","year":"2017","unstructured":"Christos Louizos, Uri Shalit, Joris M Mooij, David Sontag, Richard Zemel, and Max Welling. 2017. Causal effect inference with deep latent-variable models. Advances in neural information processing systems , Vol. 30 (2017)."},{"key":"e_1_3_2_2_17_1","doi-asserted-by":"publisher","DOI":"10.1145\/3219819.3220007"},{"key":"e_1_3_2_2_18_1","doi-asserted-by":"publisher","DOI":"10.1145\/3209978.3210104"},{"key":"e_1_3_2_2_19_1","doi-asserted-by":"publisher","DOI":"10.1080\/01621459.1987.10478441"},{"key":"e_1_3_2_2_20_1","doi-asserted-by":"publisher","DOI":"10.1198\/016214504000001880"},{"key":"e_1_3_2_2_21_1","volume-title":"International conference on machine learning. PMLR, 3076--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 International conference on machine learning. PMLR, 3076--3085."},{"key":"e_1_3_2_2_22_1","volume-title":"Adapting neural networks for the estimation of treatment effects. Advances in neural information processing systems","author":"Shi Claudia","year":"2019","unstructured":"Claudia Shi, David Blei, and Victor Veitch. 2019. Adapting neural networks for the estimation of treatment effects. Advances in neural information processing systems , Vol. 32 (2019)."},{"key":"e_1_3_2_2_23_1","volume-title":"Matching methods for causal inference: A review and a look forward. Statistical science: a review journal of the Institute of Mathematical Statistics","author":"Stuart Elizabeth A","year":"2010","unstructured":"Elizabeth A Stuart. 2010. Matching methods for causal inference: A review and a look forward. Statistical science: a review journal of the Institute of Mathematical Statistics, Vol. 25, 1 (2010), 1."},{"key":"e_1_3_2_2_24_1","doi-asserted-by":"publisher","DOI":"10.1080\/01621459.2017.1319839"},{"key":"e_1_3_2_2_25_1","doi-asserted-by":"publisher","DOI":"10.1145\/3477495.3531970"},{"key":"e_1_3_2_2_26_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-05981-0_16"},{"key":"e_1_3_2_2_27_1","volume-title":"A survey on causal inference. ACM Transactions on Knowledge Discovery from Data (TKDD)","author":"Yao Liuyi","year":"2021","unstructured":"Liuyi Yao, Zhixuan Chu, Sheng Li, Yaliang Li, Jing Gao, and Aidong Zhang. 2021. A survey on causal inference. ACM Transactions on Knowledge Discovery from Data (TKDD), Vol. 15, 5 (2021), 1--46."},{"key":"e_1_3_2_2_28_1","volume-title":"International conference on learning representations.","author":"Yoon Jinsung","year":"2018","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."},{"key":"e_1_3_2_2_29_1","doi-asserted-by":"publisher","DOI":"10.1145\/3292500.3330700"},{"key":"e_1_3_2_2_30_1","doi-asserted-by":"publisher","DOI":"10.1145\/3534678.3539198"}],"event":{"name":"WWW '24: The ACM Web Conference 2024","sponsor":["SIGWEB ACM Special Interest Group on Hypertext, Hypermedia, and Web"],"location":"Singapore Singapore","acronym":"WWW '24"},"container-title":["Companion Proceedings of the ACM Web Conference 2024"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3589335.3648320","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3589335.3648320","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,8,22]],"date-time":"2025-08-22T00:38:04Z","timestamp":1755823084000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3589335.3648320"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,5,13]]},"references-count":30,"alternative-id":["10.1145\/3589335.3648320","10.1145\/3589335"],"URL":"https:\/\/doi.org\/10.1145\/3589335.3648320","relation":{},"subject":[],"published":{"date-parts":[[2024,5,13]]},"assertion":[{"value":"2024-05-13","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}