{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T05:57:40Z","timestamp":1777615060475,"version":"3.51.4"},"publisher-location":"New York, NY, USA","reference-count":57,"publisher":"ACM","license":[{"start":{"date-parts":[[2022,8,14]],"date-time":"2022-08-14T00:00:00Z","timestamp":1660435200000},"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":[[2022,8,14]]},"DOI":"10.1145\/3534678.3539175","type":"proceedings-article","created":{"date-parts":[[2022,8,12]],"date-time":"2022-08-12T19:06:41Z","timestamp":1660331201000},"page":"4173-4183","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":2,"title":["Interpretable Personalized Experimentation"],"prefix":"10.1145","author":[{"given":"Han","family":"Wu","sequence":"first","affiliation":[{"name":"Stanford University, Stanford, CA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sarah","family":"Tan","sequence":"additional","affiliation":[{"name":"Meta, Menlo Park, CA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Weiwei","family":"Li","sequence":"additional","affiliation":[{"name":"Meta, Menlo Park, CA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mia","family":"Garrard","sequence":"additional","affiliation":[{"name":"Meta, Menlo Park, CA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Adam","family":"Obeng","sequence":"additional","affiliation":[{"name":"Unaffiliated, San Francisco, CA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Drew","family":"Dimmery","sequence":"additional","affiliation":[{"name":"University of Vienna, Vienna, Austria"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shaun","family":"Singh","sequence":"additional","affiliation":[{"name":"Unaffiliated, San Francisco, CA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hanson","family":"Wang","sequence":"additional","affiliation":[{"name":"Unaffiliated, San Francisco, CA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Daniel","family":"Jiang","sequence":"additional","affiliation":[{"name":"Meta, Menlo Park, CA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Eytan","family":"Bakshy","sequence":"additional","affiliation":[{"name":"Meta, Menlo Park, CA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2022,8,14]]},"reference":[{"key":"e_1_3_2_1_1_1","volume-title":"Optimal Policy Trees. arXiv:2012.02279","author":"Amram Maxime","year":"2020","unstructured":"Maxime Amram, Jack Dunn, and Ying Daisy Zhuo. 2020. Optimal Policy Trees. arXiv:2012.02279 (2020)."},{"key":"e_1_3_2_1_2_1","volume-title":"Kasten","author":"Assmann Susan F.","year":"2000","unstructured":"Susan F. Assmann, Stuart J. Pocock, Laura E. Enos, and Linda E. Kasten. 2000. Subgroup analysis and other (mis) uses of baseline data in clinical trials. The Lancet 355, 9209 (2000)."},{"key":"e_1_3_2_1_3_1","volume-title":"Recursive partitioning for heterogeneous causal effects. PNAS 113, 27","author":"Athey Susan","year":"2016","unstructured":"Susan Athey and Guido Imbens. 2016. Recursive partitioning for heterogeneous causal effects. PNAS 113, 27 (2016)."},{"key":"e_1_3_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.3982\/ECTA15732"},{"key":"e_1_3_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1287\/ijoo.2018.0005"},{"key":"e_1_3_2_1_6_1","unstructured":"Max Biggs Wei Sun and Markus Ettl. 2021. Model Distillation for Revenue Optimization: Interpretable Personalized Pricing. In ICML."},{"key":"e_1_3_2_1_7_1","doi-asserted-by":"crossref","unstructured":"Cristian Bucilua Rich Caruana and Alexandru Niculescu-Mizil. 2006. Model compression. In KDD.","DOI":"10.1145\/1150402.1150464"},{"key":"e_1_3_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1023\/A:1007379606734"},{"key":"e_1_3_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1002\/sim.6343"},{"key":"e_1_3_2_1_10_1","doi-asserted-by":"crossref","unstructured":"Paul Covington Jay Adams and Emre Sargin. 2016. Deep Neural Networks for YouTube Recommendations. In RecSys.","DOI":"10.1145\/2959100.2959190"},{"key":"e_1_3_2_1_11_1","unstructured":"Miroslav Dud\u00edk John Langford and Lihong Li. 2011. Doubly Robust Policy Evaluation and Learning. In ICML."},{"key":"e_1_3_2_1_12_1","volume-title":"Briton Park, Mian Wei, Kevin Horgan, David Madigan, and Bin Yu.","author":"Dwivedi Raaz","year":"2020","unstructured":"Raaz Dwivedi, Yan Shuo Tan, Briton Park, Mian Wei, Kevin Horgan, David Madigan, and Bin Yu. 2020. Stable discovery of interpretable subgroups via calibration in causal studies. International Statistical Review 88 (2020)."},{"key":"e_1_3_2_1_13_1","unstructured":"Charles Elkan. 2001. The Foundations of Cost-Sensitive Learning. In IJCAI."},{"key":"e_1_3_2_1_14_1","volume-title":"Jeremy MG Taylor, and Stephen J Ruberg","author":"Foster Jared C","year":"2011","unstructured":"Jared C Foster, Jeremy MG Taylor, and Stephen J Ruberg. 2011. Subgroup identification from randomized clinical trial data. Stat Med 30, 24 (2011)."},{"key":"e_1_3_2_1_15_1","doi-asserted-by":"crossref","unstructured":"Antonino Freno. 2017. Practical Lessons from Developing a Large-Scale Recommender System at Zalando. In RecSys.","DOI":"10.1145\/3109859.3109897"},{"key":"e_1_3_2_1_16_1","doi-asserted-by":"crossref","unstructured":"Florent Garcin Christos Dimitrakakis and Boi Faltings. 2013. Personalized News Recommendation with Context Trees. In RecSys.","DOI":"10.1145\/2507157.2507166"},{"key":"e_1_3_2_1_17_1","doi-asserted-by":"crossref","unstructured":"Mihajlo Grbovic and Haibin Cheng. 2018. Real-time personalization using embeddings for search ranking at airbnb. In KDD.","DOI":"10.1145\/3219819.3219885"},{"key":"e_1_3_2_1_18_1","volume-title":"A review of multi-objective optimization: Methods and its applications. Cogent Engineering 5, 1","author":"Gunantara Nyoman","year":"2018","unstructured":"Nyoman Gunantara. 2018. A review of multi-objective optimization: Methods and its applications. Cogent Engineering 5, 1 (2018)."},{"key":"e_1_3_2_1_19_1","unstructured":"Tamir Hazan Joseph Keshet and David McAllester. 2010. Direct Loss Minimization for Structured Prediction. In NeurIPS."},{"key":"e_1_3_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.1198\/jcgs.2010.08162"},{"key":"e_1_3_2_1_21_1","volume-title":"Accessed","author":"Hillstrom Kevin","year":"2008","unstructured":"Kevin Hillstrom. 2008. MineThatData E-Mail Analytics And Data Mining Challenge. https:\/\/www.uplift-modeling.com\/en\/v0.3.1\/api\/datasets\/fetch_hillstrom. html. Accessed October 19, 2021."},{"key":"e_1_3_2_1_22_1","volume-title":"NeurIPS Deep Learning Workshop.","author":"Hinton Geoffrey","year":"2014","unstructured":"Geoffrey Hinton, Oriol Vinyals, and Jeff Dean. 2014. Distilling the Knowledge in a Neural Network. In NeurIPS Deep Learning Workshop."},{"key":"e_1_3_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.1214\/12-AOAS593"},{"key":"e_1_3_2_1_24_1","doi-asserted-by":"publisher","DOI":"10.1093\/pan\/mpq035"},{"key":"e_1_3_2_1_25_1","volume-title":"Learning Optimal Prescriptive Trees from Observational Data. arXiv:2108.13628","author":"Jo Nathanael","year":"2021","unstructured":"Nathanael Jo, Sina Aghaei, Andr\u00e9s G\u00f3mez, and Phebe Vayanos. 2021. Learning Optimal Prescriptive Trees from Observational Data. arXiv:2108.13628 (2021)."},{"key":"e_1_3_2_1_26_1","unstructured":"Fredrik Johansson Uri Shalit and David Sontag. 2016. Learning representations for counterfactual inference. In ICML."},{"key":"e_1_3_2_1_27_1","unstructured":"Nathan Kallus. 2017. Recursive partitioning for personalization using observational data. In ICML."},{"key":"e_1_3_2_1_28_1","unstructured":"Nathan Kallus and Angela Zhou. 2018. Policy evaluation and optimization with continuous treatments. In AISTATS."},{"key":"e_1_3_2_1_29_1","volume-title":"Optimal doubly robust estimation of heterogeneous causal effects. arXiv:2004.14497","author":"Kennedy Edward H.","year":"2020","unstructured":"Edward H. Kennedy. 2020. Optimal doubly robust estimation of heterogeneous causal effects. arXiv:2004.14497 (2020)."},{"key":"e_1_3_2_1_30_1","volume-title":"Who should be treated? empirical welfare maximization methods for treatment choice. Econometrica 86, 2","author":"Kitagawa Toru","year":"2018","unstructured":"Toru Kitagawa and Aleksey Tetenov. 2018. Who should be treated? empirical welfare maximization methods for treatment choice. Econometrica 86, 2 (2018)."},{"key":"e_1_3_2_1_31_1","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.1804597116"},{"key":"e_1_3_2_1_32_1","doi-asserted-by":"crossref","unstructured":"Akos Lada Alexander Peysakhovich Diego Aparicio and Michael Bailey. 2019. Observational data for heterogeneous treatment effects with application to recommender systems. In EC.","DOI":"10.1145\/3328526.3329558"},{"key":"e_1_3_2_1_33_1","doi-asserted-by":"publisher","DOI":"10.1016\/0020-0190(76)90095-8"},{"key":"e_1_3_2_1_34_1","unstructured":"Hyun-Suk Lee Yao Zhang William Zame Cong Shen Jang-Won Lee and Mihaela van der Schaar. 2020. Robust recursive partitioning for heterogeneous treatment effects with uncertainty quantification. In NeurIPS."},{"key":"e_1_3_2_1_35_1","first-page":"145","article-title":"Bayesian Optimization for Policy Search via Online-Offline Experimentation","volume":"20","author":"Letham Benjamin","year":"2019","unstructured":"Benjamin Letham and Eytan Bakshy. 2019. Bayesian Optimization for Policy Search via Online-Offline Experimentation. JMLR 20 (2019), 145--1.","journal-title":"JMLR"},{"key":"e_1_3_2_1_36_1","doi-asserted-by":"publisher","DOI":"10.1002\/widm.1326"},{"key":"e_1_3_2_1_37_1","doi-asserted-by":"crossref","unstructured":"Maggie Makar Adith Swaminathan and Emre K?c?man. 2019. A distillation approach to data efficient individual treatment effect estimation. In AAAI.","DOI":"10.1609\/aaai.v33i01.33014544"},{"key":"e_1_3_2_1_38_1","volume-title":"Accessed","year":"2021","unstructured":"MedicineNet. 2021. Liver Function Tests (Normal, Low, and High Ranges & Results). https:\/\/www.medicinenet.com\/liver_blood_tests\/article.htm. Accessed October 19, 2021."},{"key":"e_1_3_2_1_39_1","volume-title":"The Optimal Dynamic Treatment Rule SuperLearner: Considerations, Performance, and Application. arXiv:2101.12326","author":"Montoya Lina","year":"2021","unstructured":"Lina Montoya, Mark van der Laan, Alexander Luedtke, Jennifer Skeem, Jeremy Coyle, and Maya Petersen. 2021. The Optimal Dynamic Treatment Rule SuperLearner: Considerations, Performance, and Application. arXiv:2101.12326 (2021)."},{"key":"e_1_3_2_1_40_1","doi-asserted-by":"publisher","DOI":"10.1145\/3368555.3384456"},{"key":"e_1_3_2_1_41_1","volume-title":"Sur les applications de la th\u00e9orie des probabilit\u00e9s aux experiences agricoles: Essai des principes. Roczniki Nauk Rolniczych 10","author":"Neyman Jersey","year":"1923","unstructured":"Jersey Neyman. 1923. Sur les applications de la th\u00e9orie des probabilit\u00e9s aux experiences agricoles: Essai des principes. Roczniki Nauk Rolniczych 10 (1923)."},{"key":"e_1_3_2_1_42_1","doi-asserted-by":"publisher","DOI":"10.1093\/biomet\/asaa076"},{"key":"e_1_3_2_1_43_1","doi-asserted-by":"publisher","DOI":"10.1214\/10-AOS864"},{"key":"e_1_3_2_1_44_1","doi-asserted-by":"publisher","DOI":"10.1037\/h0037350"},{"key":"e_1_3_2_1_45_1","unstructured":"D. Sculley Gary Holt Daniel Golovin Eugene Davydov Todd Phillips Dietmar Ebner Vinay Chaudhary Michael Young Jean-Fran\u00e7ois Crespo and Dan Dennison. 2015. Hidden Technical Debt in Machine Learning Systems. In NeurIPS."},{"key":"e_1_3_2_1_46_1","doi-asserted-by":"crossref","unstructured":"Paras Sheth Ujun Jeong Ruocheng Guo Huan Liu and K Sel\u00e7uk Candan. 2021. CauseBox: A Causal Inference Toolbox for Benchmarking Treatment Effect Estimators with Machine Learning Methods. In CIKM.","DOI":"10.1145\/3459637.3481974"},{"key":"e_1_3_2_1_47_1","doi-asserted-by":"publisher","DOI":"10.1561\/2200000068"},{"key":"e_1_3_2_1_48_1","doi-asserted-by":"publisher","DOI":"10.1002\/sim.2825"},{"key":"e_1_3_2_1_49_1","doi-asserted-by":"crossref","unstructured":"Adith Swaminathan and Thorsten Joachims. 2015. Counterfactual risk minimization: Learning from logged bandit feedback. In ICML. 814--823.","DOI":"10.1145\/2740908.2742564"},{"key":"e_1_3_2_1_50_1","unstructured":"Xiaocheng Tang Fan Zhang Zhiwei Qin Yansheng Wang Dingyuan Shi Bingchen Song Yongxin Tong Hongtu Zhu and Jieping Ye. 2021. Value Function is All You Need: A Unified Learning Framework for Ride Hailing Platforms. In KDD."},{"key":"e_1_3_2_1_51_1","doi-asserted-by":"crossref","unstructured":"Ye Tu Kinjal Basu Cyrus DiCiccio Romil Bansal Preetam Nandy Padmini Jaikumar and Shaunak Chatterjee. 2021. Personalized Treatment Selection Using Causal Heterogeneity. In WWW.","DOI":"10.1145\/3442381.3450075"},{"key":"e_1_3_2_1_52_1","doi-asserted-by":"publisher","DOI":"10.1080\/01621459.2017.1319839"},{"key":"e_1_3_2_1_53_1","volume-title":"Causal rule sets for identifying subgroups with enhanced treatment effect. INFORMS Journal on Computing","author":"Wang Tong","year":"2021","unstructured":"Tong Wang and Cynthia Rudin. 2021. Causal rule sets for identifying subgroups with enhanced treatment effect. INFORMS Journal on Computing (2021)."},{"key":"e_1_3_2_1_54_1","doi-asserted-by":"publisher","DOI":"10.1016\/S0140-6736(20)31022-9"},{"key":"e_1_3_2_1_55_1","unstructured":"Yuxiang Xie Nanyu Chen and Xiaolin Shi. 2018. False discovery rate controlled heterogeneous treatment effect detection for online controlled experiments. In KDD."},{"key":"e_1_3_2_1_56_1","doi-asserted-by":"publisher","DOI":"10.1080\/01621459.2012.695674"},{"key":"e_1_3_2_1_57_1","volume-title":"Offline multi-action policy learning: Generalization and optimization. arXiv:1810.04778","author":"Zhou Zhengyuan","year":"2018","unstructured":"Zhengyuan Zhou, Susan Athey, and Stefan Wager. 2018. Offline multi-action policy learning: Generalization and optimization. arXiv:1810.04778 (2018)."}],"event":{"name":"KDD '22: The 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","location":"Washington DC USA","acronym":"KDD '22","sponsor":["SIGMOD ACM Special Interest Group on Management of Data","SIGKDD ACM Special Interest Group on Knowledge Discovery in Data"]},"container-title":["Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3534678.3539175","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3534678.3539175","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T19:02:59Z","timestamp":1750186979000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3534678.3539175"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,8,14]]},"references-count":57,"alternative-id":["10.1145\/3534678.3539175","10.1145\/3534678"],"URL":"https:\/\/doi.org\/10.1145\/3534678.3539175","relation":{},"subject":[],"published":{"date-parts":[[2022,8,14]]},"assertion":[{"value":"2022-08-14","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}