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Elizabeth L. Ogburn develops methods for causal inference in complex data, particularly focusing on social network data and unmeasured confounding. Her research interests lie in creating methods that describe behavior when traditional statistical assumptions are not fulfilled. She has worked on characterizing the bias in results due to misclassification, addressing situations where violations of the assumption that all variables are measured accurately occur. Her expertise includes semiparametric estimation and the use of instrumental variables models, which are particularly useful for overcoming violations of the assumption of no unmeasured confounding. Currently, her main research is centered on developing new statistical methods for causal inference in the presence of interference, where one subject's treatment can affect the outcomes of other subjects. This work is crucial for understanding complex social networks where independence of observations is violated.
Department of Pathology - PhD in Pathobiology. GRE is not required.