{"671508":{"#nid":"671508","#data":{"type":"event","title":"ISyE Seminar - Shuangning Li","body":[{"value":"\u003Ch3\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cstrong\u003E\u003Cspan\u003E\u003Cspan\u003ETitle\u003C\/span\u003E\u003C\/span\u003E\u003C\/strong\u003E\u003Cspan\u003E\u003Cspan\u003E: \u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/h3\u003E\r\n\r\n\u003Cp\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003EModeling\u0026nbsp;Interference\u0026nbsp;for Policy Evaluation in Stochastic Systems\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/p\u003E\r\n\r\n\u003Ch3\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cstrong\u003E\u003Cspan\u003EAbstract\u003C\/span\u003E\u003C\/strong\u003E\u003Cspan\u003E:\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/h3\u003E\r\n\r\n\u003Cp\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003EInterference is a phenomenon where the treatment of one unit may affect the outcomes of other units. It is a major consideration for accurate policy evaluation in stochastic systems. Although this may seem intractable in a non-parametric causal inference setting, I will demonstrate that in many problems, some lightweight modeling can significantly aid in capturing and quantifying these interference effects. In particular, I will discuss two different forms of interference:\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E(1) Network interference. In the network interference model, units are represented as vertices on an exposure graph (for example, a social network). In this model, the treatment assigned to one unit may affect the outcomes of other units connected to it through edges in the graph. I will discuss large-sample asymptotics for treatment effect estimation under network interference, where the exposure graph is a random draw from a graphon.\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E(2) Congestion induced interference. In service systems, stochastic congestion can arise from temporarily limited supply and\/or demand. Such congestion gives rise to interference between the waiting customers, and analytic strategies that do not account for this interference may be biased. I will discuss the potential of using knowledge about the congestion mechanism to design and analyze experiments in the presence of stochastic congestion.\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/p\u003E\r\n\r\n\u003Ch3\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cstrong\u003E\u003Cspan\u003EBio\u003C\/span\u003E\u003C\/strong\u003E\u003Cspan\u003E: \u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/h3\u003E\r\n\r\n\u003Cp\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003EI am currently a postdoctoral fellow working with Professor Susan Murphy in the Department of Statistics at Harvard University. Prior to this, I earned my Ph.D. from the Department of Statistics at Stanford University, where I was advised by Professors Emmanuel Cand\u00e8s and Stefan Wager.\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\r\n","summary":"","format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Ch3\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cstrong\u003E\u003Cspan\u003EAbstract\u003C\/span\u003E\u003C\/strong\u003E\u003Cspan\u003E: \u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/h3\u003E\r\n\r\n\u003Cp\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003EInterference is a phenomenon where the treatment of one unit may affect the outcomes of other units. It is a major consideration for accurate policy evaluation in stochastic systems. Although this may seem intractable in a non-parametric causal inference setting, I will demonstrate that in many problems, some lightweight modeling can significantly aid in capturing and quantifying these interference effects. In particular, I will discuss two different forms of interference:\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E(1) Network interference. In the network interference model, units are represented as vertices on an exposure graph (for example, a social network). In this model, the treatment assigned to one unit may affect the outcomes of other units connected to it through edges in the graph. I will discuss large-sample asymptotics for treatment effect estimation under network interference, where the exposure graph is a random draw from a graphon.\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cspan\u003E\u003Cspan\u003E\u003Cspan\u003E(2) Congestion induced interference. In service systems, stochastic congestion can arise from temporarily limited supply and\/or demand. Such congestion gives rise to interference between the waiting customers, and analytic strategies that do not account for this interference may be biased. I will discuss the potential of using knowledge about the congestion mechanism to design and analyze experiments in the presence of stochastic congestion.\u003C\/span\u003E\u003C\/span\u003E\u003C\/span\u003E\u003C\/p\u003E\r\n","format":"limited_html"}],"field_summary_sentence":[{"value":"Modeling Interference for Policy Evaluation in Stochastic Systems"}],"uid":"34977","created_gmt":"2023-12-11 14:17:46","changed_gmt":"2023-12-11 14:17:46","author":"Julie Smith","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2024-01-11T11:00:00-05:00","event_time_end":"2024-01-11T12:00:00-05:00","event_time_end_last":"2024-01-11T12:00:00-05:00","gmt_time_start":"2024-01-11 16:00:00","gmt_time_end":"2024-01-11 17:00:00","gmt_time_end_last":"2024-01-11 17:00:00","rrule":null,"timezone":"America\/New_York"},"location":"ISyE Groseclose 402","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":"174045","name":"Graduate students"},{"id":"78751","name":"Undergraduate students"}],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[],"email":[],"slides":[],"orientation":[],"userdata":""}}}