{"664033":{"#nid":"664033","#data":{"type":"event","title":"ISyE Seminar - Tianyi Peng","body":[{"value":"\u003Ch3\u003E\u003Cstrong\u003ETitle: \u003C\/strong\u003E\u003C\/h3\u003E\r\n\r\n\u003Cp\u003EExperimentation Platforms and Learning Treatment Effects in Panels\u003C\/p\u003E\r\n\r\n\u003Ch3\u003E\u003Cstrong\u003EAbstract:\u003C\/strong\u003E\u003C\/h3\u003E\r\n\r\n\u003Cp\u003EExperiments in brick-and-mortar retail are contaminated for myriad reasons. Pragmatic inference in such settings is more akin to learning from observational data, as opposed to the typical setup one might consider for a carefully designed randomized experiment. So motivated, we consider the problem of causal inference in panels with general intervention patterns that may depend on the historical data. We provide a novel, near-complete solution to this problem that allows for rate-optimal recovery of treatment effects. Our work generalizes the outcome model of the difference-in-difference paradigm and expands the applicability of the synthetic-control paradigm. In doing so, we provide a novel de-biasing analysis that addresses the low-rank matrix regression with non-random intervention patterns and noise; a non-trivial feature of independent interest.\u0026nbsp; Our algorithms form the core of a new testing platform we co-developed with a USD 100B drink company, which increased revenue by millions of dollars monthly in Mexico alone.\u0026nbsp;\u003C\/p\u003E\r\n\r\n\u003Ch3\u003E\u003Cstrong\u003EBio:\u0026nbsp;\u003C\/strong\u003E\u003C\/h3\u003E\r\n\r\n\u003Cp\u003ETianyi Peng is a Ph.D. student at MIT. He is advised by Vivek Farias, and also mentored by Andrew Li.\u0026nbsp;He is broadly interested in developing algorithms for learning and inference in large-scale dynamic decision-making systems. In particular, he is interested in\u0026nbsp;developing next-generation experimentation platforms, which provide scalable, low-cost solutions for discovering beneficial strategies\/policies. In translating these ideas, he is engaged with Anheuser-Busch InBev, Takeda Pharmaceuticals, TikTok, and Liberty Mutual. His work has been recognized as a\u0026nbsp;finalist for the MSOM Student Paper\u0026nbsp;Competition (2022), and\u0026nbsp;has won\u0026nbsp;the INFORMS Daniel H. Wagner Prize (2022), Applied Probability Society Best Student Paper Prize (2022), Jeff McGill Student Paper Award (2022)\u0026nbsp;and the best thesis award at Tsinghua where he\u0026nbsp;graduated with the 2017 Yao Class.\u0026nbsp;\u003C\/p\u003E\r\n","summary":null,"format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Ch3\u003E\u003Cstrong\u003EAbstract:\u003C\/strong\u003E\u003C\/h3\u003E\r\n\r\n\u003Cp\u003EExperiments in brick-and-mortar retail are contaminated for myriad reasons. Pragmatic inference in such settings is more akin to learning from observational data, as opposed to the typical setup one might consider for a carefully designed randomized experiment. So motivated, we consider the problem of causal inference in panels with general intervention patterns that may depend on the historical data. We provide a novel, near-complete solution to this problem that allows for rate-optimal recovery of treatment effects. Our work generalizes the outcome model of the difference-in-difference paradigm and expands the applicability of the synthetic-control paradigm. In doing so, we provide a novel de-biasing analysis that addresses the low-rank matrix regression with non-random intervention patterns and noise; a non-trivial feature of independent interest.\u0026nbsp; Our algorithms form the core of a new testing platform we co-developed with a USD 100B drink company, which increased revenue by millions of dollars monthly in Mexico alone.\u0026nbsp;\u003C\/p\u003E\r\n","format":"limited_html"}],"field_summary_sentence":[{"value":"Experimentation Platforms and Learning Treatment Effects in Panels"}],"uid":"34977","created_gmt":"2022-12-21 18:20:49","changed_gmt":"2022-12-22 14:35:24","author":"Julie Smith","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2023-01-12T11:00:00-05:00","event_time_end":"2023-01-12T12:00:00-05:00","event_time_end_last":"2023-01-12T12:00:00-05:00","gmt_time_start":"2023-01-12 16:00:00","gmt_time_end":"2023-01-12 17:00:00","gmt_time_end_last":"2023-01-12 17:00:00","rrule":null,"timezone":"America\/New_York"},"extras":[],"groups":[{"id":"1242","name":"School of Industrial and Systems Engineering (ISYE)"}],"categories":[],"keywords":[],"core_research_areas":[],"news_room_topics":[],"event_categories":[{"id":"1795","name":"Seminar\/Lecture\/Colloquium"}],"invited_audience":[{"id":"78761","name":"Faculty\/Staff"},{"id":"177814","name":"Postdoc"},{"id":"78771","name":"Public"},{"id":"174045","name":"Graduate students"},{"id":"78751","name":"Undergraduate students"}],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[],"email":[],"slides":[],"orientation":[],"userdata":""}}}