{"675463":{"#nid":"675463","#data":{"type":"event","title":"Phd Defense by Yuqin Yang","body":[{"value":"\u003Cp\u003E\u003Cstrong\u003ETitle:\u0026nbsp;Causal Discovery from Observational Data in the Presence of Latent Confounders and Complex Data\u003C\/strong\u003E\u003C\/p\u003E\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\u003Cp\u003E\u003Cstrong\u003EDate:\u0026nbsp;Monday, July 22\u003C\/strong\u003E\u003C\/p\u003E\u003Cp\u003E\u003Cstrong\u003ETime:\u0026nbsp;11:00 AM\u0026nbsp;ET\u003C\/strong\u003E\u003C\/p\u003E\u003Cp\u003ELocation: Coda C1115 Druid Hills \u0026amp; \u003Ca href=\u0022https:\/\/gatech.zoom.us\/j\/95995801503?pwd=58tyk27aFK6gVb1OKKIHAsHxZAyOKx.1\u0026amp;from=addon\u0022\u003EZoom\u003C\/a\u003E\u003C\/p\u003E\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\u003Cp\u003E\u003Cstrong\u003EYuqin Yang\u003C\/strong\u003E\u003C\/p\u003E\u003Cp\u003EMachine Learning Ph.D. Student\u003C\/p\u003E\u003Cp\u003ESchool of Mathematics\u003Cbr\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. Prof. Mark Davenport (Advisor)\u003C\/p\u003E\u003Cp\u003E2. Prof. Negar Kiyavash (Co-advisor)\u003C\/p\u003E\u003Cp\u003E3. Prof. Yao Xie\u003C\/p\u003E\u003Cp\u003E4. Prof. Ashwin Pananjady\u003C\/p\u003E\u003Cp\u003E5. Prof. Kun Zhang\u003C\/p\u003E\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\u003Cp\u003E\u003Cstrong\u003EAbstract\u003C\/strong\u003E\u003C\/p\u003E\u003Cp\u003ECausal discovery aims to recover causal relationships among variables of interest from only observational data. However, there are complexities in real-life data that make causal discovery even more challenging. Some of the main sources of data complexity include latent confounding, deterministic relations, measurement error, and data heterogeneity. The majority of causal discovery methods assume that these complexities are absent in the system. Naturally, naive applications of these approaches to settings that indeed are subject to data complexity issues lead to detecting spurious or erroneous causal links among variables of interest.\u003C\/p\u003E\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\u003Cp\u003EThe focus of the dissertation is on developing causal discovery methods that are capable of handling these data complexities. Specifically: (1) We study the problem of causal discovery in linear causal models with deterministic relations and latent confounding. (2) We study the problem of causal discovery in linear causal models in the presence of latent confounding and\/or measurement error. (3) We study the problem of learning the unknown intervention targets in linear or nonlinear causal models from a collection of interventional data obtained from multiple environments, both under causal sufficiency assumption and in the presence of latent confounding.\u003C\/p\u003E\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E","summary":"","format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Cp\u003E\u003Cstrong\u003ECausal Discovery from Observational Data in the Presence of Latent Confounders and Complex Data\u003C\/strong\u003E\u003C\/p\u003E","format":"limited_html"}],"field_summary_sentence":[{"value":"Causal Discovery from Observational Data in the Presence of Latent Confounders and Complex Data"}],"uid":"27707","created_gmt":"2024-07-15 18:41:32","changed_gmt":"2024-07-15 18:41:32","author":"Tatianna Richardson","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2024-07-22T11:00:00-04:00","event_time_end":"2024-07-22T13:00:00-04:00","event_time_end_last":"2024-07-22T13:00:00-04:00","gmt_time_start":"2024-07-22 15:00:00","gmt_time_end":"2024-07-22 17:00:00","gmt_time_end_last":"2024-07-22 17:00:00","rrule":null,"timezone":"America\/New_York"},"location":"Coda C1115 Druid Hills \u0026 Zoom ","extras":[],"groups":[{"id":"221981","name":"Graduate Studies"}],"categories":[],"keywords":[{"id":"100811","name":"Phd Defense"}],"core_research_areas":[],"news_room_topics":[],"event_categories":[{"id":"1788","name":"Other\/Miscellaneous"}],"invited_audience":[{"id":"78771","name":"Public"}],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[],"email":[],"slides":[],"orientation":[],"userdata":""}}}