{"681783":{"#nid":"681783","#data":{"type":"event","title":"PhD Defense | Estimation of Treatment Effects in Matching Marketplaces under Interference","body":[{"value":"\u003Cp\u003E\u003Cstrong\u003ETitle: Estimation of Treatment Effects in Matching Marketplaces under Interference\u003C\/strong\u003E\u003C\/p\u003E\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\u003Cp\u003E\u003Cstrong\u003EDate:\u003C\/strong\u003E Thursday, April 24th, 2025\u003C\/p\u003E\u003Cp\u003E\u003Cstrong\u003ETime:\u003C\/strong\u003E 11:00 am - 1:00 pm (EDT)\u0026nbsp;\u003C\/p\u003E\u003Cp\u003ELocation: Groseclose 402\u003C\/p\u003E\u003Cp\u003EMS Teams: \u003Ca href=\u0022https:\/\/teams.microsoft.com\/l\/meetup-join\/19%3ameeting_OTQ4YTI0N2UtZjg3OC00NmE3LWE4NjYtZjU0OWMxMWZlYzBj%40thread.v2\/0?context=%7b%22Tid%22%3a%22482198bb-ae7b-4b25-8b7a-6d7f32faa083%22%2c%22Oid%22%3a%229db639d3-27a7-4596-aa18-7d25c696b63a%22%7d\u0022\u003Elink\u003C\/a\u003E\u003C\/p\u003E\u003Cp\u003EMeeting ID: 269 191 089 348\u003C\/p\u003E\u003Cp\u003EPasscode: Wx7DK9FU\u003C\/p\u003E\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\u003Cp\u003E\u003Cstrong\u003EKleanthis Karakolios\u003C\/strong\u003E\u003C\/p\u003E\u003Cp\u003EMachine Learning PhD Student\u003C\/p\u003E\u003Cp\u003ESchool of Electrical and Computer Engineering\u003C\/p\u003E\u003Cp\u003EGeorgia Institute of Technology\u003C\/p\u003E\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\u003Cp\u003E\u003Cstrong\u003ECommittee\u003C\/strong\u003E\u003C\/p\u003E\u003Cp\u003E1 Dr. Arthur Delarue (ISyE, Advisor)\u003C\/p\u003E\u003Cp\u003E2 Dr. Xin Chen (ISyE)\u003C\/p\u003E\u003Cp\u003E3 Dr. Juba Ziani (ISyE)\u003C\/p\u003E\u003Cp\u003E4 Dr. Joel Sokol (ISyE)\u003C\/p\u003E\u003Cp\u003E5 Dr. Andre Calmon (Scheller CoB)\u003C\/p\u003E\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\u003Cp\u003E\u003Cstrong\u003EAbstract\u003C\/strong\u003E\u003C\/p\u003E\u003Cp\u003EThis thesis develops methodological tools to mitigate interference bias in randomized experiments conducted within matching marketplaces. Interference occurs when treated and untreated units interact, violating standard causal assumptions and introducing bias into treatment effect estimates. The work addresses three key experimental settings: pricing interventions, heterogeneous treatment effect (HTE) estimation, and treatment effect bounding. First, the thesis analyzes pricing experiments and demonstrates that standard estimators are systematically biased, with the direction and magnitude of bias depending on whether treated and untreated units are matched differently. A shadow price-based estimator, which leverages dual variables from the platform\u2019s matching algorithm, is shown to significantly reduce this bias. Second, in the context of HTE estimation, the thesis distinguishes between settings where the effect is uniquely defined and those where it is not. In the latter case, the problem is recast as a policy decision task, and shadow price-guided decision rules are proposed to improve rollout strategies. Finally, the thesis introduces a bounding framework that derives tight upper and lower bounds on treatment effects using a single experiment. The resulting point estimator, based on these bounds, achieves substantially lower variance and reduced root mean squared error relative to existing methods, offering a practical tool for managing the bias-variance trade-off in experimental evaluation.\u003C\/p\u003E","summary":"","format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Cp\u003E\u003Cstrong\u003EEstimation of Treatment Effects in Matching Marketplaces under Interference\u003C\/strong\u003E\u003C\/p\u003E","format":"limited_html"}],"field_summary_sentence":[{"value":"Kleanthis Karakolios - Machine Learning PhD Student - School of Electrical and Computer Engineering"}],"uid":"36518","created_gmt":"2025-04-15 12:16:23","changed_gmt":"2025-04-15 12:17:04","author":"shatcher8","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2025-04-24T11:00:00-04:00","event_time_end":"2025-04-24T13:00:00-04:00","event_time_end_last":"2025-04-24T13:00:00-04:00","gmt_time_start":"2025-04-24 15:00:00","gmt_time_end":"2025-04-24 17:00:00","gmt_time_end_last":"2025-04-24 17:00:00","rrule":null,"timezone":"America\/New_York"},"location":"Groseclose 402","extras":[],"groups":[{"id":"576481","name":"ML@GT"}],"categories":[],"keywords":[],"core_research_areas":[],"news_room_topics":[],"event_categories":[],"invited_audience":[],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[],"email":[],"slides":[],"orientation":[],"userdata":""}}}