[{"id":17192,"title":"Deep Learning Based Causal Inference for Large-Scale Combinatorial Experiments: Theory and Empirical Evidence","permalink":"https:\/\/bschool.nus.edu.sg\/biz-events\/event\/deep-learning-based-causal-inference-for-large-scale-combinatorial-experiments-theory-and-empirical-evidence\/","category":"Seminars and talks","event_dept":{"value":"analytics-operations","label":"Analytics & Operations"},"event_sec_dept":false,"event_details":{"event_start_date":"31 March 2023","event_end_date":"31 March 2023","event_start_time":"10:00 am","event_end_time":"11:30 am","event_dress_code":"NA"},"event_loc":{"eve_address_selection":"5","eve_location_1":{"eve_org":"NUS Business School","eve_build":"Mochtar Riady Building","eve_room":"3-2","eve_add":"15 Kent Ridge Drive","eve_count":"Singapore","eve_copos":119245,"eve_map_url":"https:\/\/goo.gl\/maps\/Q1kyjwxHNE22"},"eve_location_2":{"eve_org":"Shaw Foundation Alumni House","eve_build":"","eve_room":"Clove and Lemongrass Room Level 2","eve_add":"11 Kent Ridge Drive","eve_count":"Singapore","eve_copos":119244,"eve_map_url":"https:\/\/goo.gl\/maps\/docgThkDWFxKdb9c7"},"eve_location_3":{"eve_org":"Hon Sui Sen Memorial Library Auditorium","eve_build":"","eve_room":"","eve_add":"1 Hon Sui Sen Drive","eve_count":"Singapore","eve_copos":117588,"eve_map_url":"https:\/\/goo.gl\/maps\/NJjWK4RMpC92"},"eve_location_4":{"eve_org":"NUSS Kent Ridge Guild House","eve_build":"","eve_room":"Dalvey Room","eve_add":"9 Kent Ridge Drive","eve_count":"Singapore","eve_copos":119241,"eve_map_url":"https:\/\/goo.gl\/maps\/nXn2Luh96pH2"},"eve_location_5":{"eve_org":"Institute of Data Science","eve_build":"Innovation 4.0","eve_room":"I4-01-03 (Level 1 Seminar Room) ","eve_add":"3 Research Link","eve_count":"Singapore","eve_copos":"117602","eve_map_url":"https:\/\/goo.gl\/maps\/i1xocvvDh27QUXem7"},"eve_location_6":{"eve_org":"","eve_build":"","eve_room":"","eve_add":"","eve_count":"","eve_copos":"","eve_map_url":""},"eve_location_7":""},"event_introduction":"","event_short_intro":"","event_topic":null,"event_banner":false,"event_external_url":"","event_registration_details":{"event_registration_form":false,"event_registration_message":"","event_registration_deadline":null,"eve_registration_url":"","event_form":"","event_registration_ack":""},"event_speaker":[{"event_speaker_name":"Zhang Renyu, Philip","event_speaker_designation":"Associate Professor","event_speaker_affiliation":"The Chinese University of Hong Kong Business School","event_speaker_picture":false,"event_speaker_url":"","event_speaker_introduction":"

Renyu (Philip) Zhang has been an Associate Professor (with tenure) at the Department of Decision Sciences and Managerial Economics, The Chinese University of Hong Kong Business School since September 2022. He is also an economist and Tech Lead at Kwai, one of the world\u2019s largest online video-sharing and live-streaming platforms. Philip\u2019s recent research focuses on developing data science methodologies (e.g., data-driven optimization, causal inference, and machine learning) to evaluate and optimize the operations strategies in the contexts of\u00a0online platforms and marketplaces,\u00a0sharing economy, and\u00a0social networks, especially their recommendation, advertising,\u00a0pricing,\u00a0and matching policies.\u00a0His research works have appeared in top business journals such as Management Science,\u00a0Operations Research,\u00a0and Manufacturing & Service Operations Management, and have been recognized by\u00a0various research awards of the INFORMS and POMS communities.\u00a0His research projects have been funded by various funding agencies including HK RGC, NSFC, SMEC, and STCSM.\u00a0 Philip serves as a Senior Editor for Production and Operations Management, and an Associate Editor for Naval Research Logistics. He has also developed data science and economics frameworks to evaluate and optimize the user growth strategy and the platform ecosystem of Kwai. Prior to joining CUHK, Philip was an Assistant Professor of Operations Management at New York University Shanghai between 2016 and 2022. Please visit Philip\u2019s personal website for more about him: https:\/\/rphilipzhang.github.io\/rphilipzhang\/<\/a><\/p>\n"}],"event_agenda":false,"event_photo_gallery":false,"event_presentations":false,"event_custom_heading":[{"event_custom_title":"Abstract","event_custom_details":"

Large-scale online platforms launch hundreds of randomized experiments (a.k.a. A\/B tests) every day to iterate their operations and marketing strategies, while the combinations of these treatments are typically not exhaustively tested. It triggers an important question of both academic and practical interests: Without observing the outcomes of all treatment combinations, how to estimate the causal effect of any treatment combination and identify the optimal treatment combination? We develop a novel framework combining deep learning and doubly robust estimation to estimate the causal effect of any treatment combination for each user on the platform when observing only a small subset of treatment combinations. Our proposed framework (called debiased deep learning, DeDL) exploits Neyman orthogonality and combines interpretable and flexible structural layers in deep learning. We prove theoretically that this framework yields efficient, consistent, and asymptotically normal estimators under mild assumptions, thus allowing for identifying the best treatment combination when only observing a few combinations. To empirically validate our method, we then collaborate with a large-scale video-sharing platform and implement our framework for three experiments involving three treatments where each combination of treatments is tested. When only observing a subset of treatment combinations, our DeDL approach significantly outperforms other benchmarks to accurately estimate and infer the average treatment effect (ATE) of any treatment combination, and to identify the optimal treatment combination.<\/p>\n

Key words: Deep Learning; Double Machine Learning; Causal Inference; Field Experiments; Experimentation on Online Platforms<\/em><\/p>\n"}],"event_enquiry_details":{"event_enq_full_name":"","event_enq_department":"","event_enq_email":"","event_enq_telephone":"","event_enq_website":""}}]