Hosted by the GPH Department of Biostatistics
Nonsense associations can arise when an exposure and an outcome of interest exhibit similar patterns of dependence. Confounding is present when potential outcomes are not independent of treatment. This talk, Dr. Elizabeth Ogburn will describe how understanding the connection between these two phenomena leads to insights in three areas: causal inference with multiple treatments and unmeasured confounding; causal and statistical inference with social network data; and causal inference with spatial data.
About the Speaker:
Elizabeth Ogburn, PhD is an Associate Professor of Biostatistics at the Johns Hopkins Bloomberg School of Public Health, whose research is in causal inference and epidemiologic methods. Broadly, she is interested in developing methods for and describing the behavior of traditional statistical machinery when standard assumptions are not met. Dr. Ogburn has worked on characterizing the bias that results from misclassification, i.e. violations of the assumption that variables were measured accurately. She has also worked on semiparametric estimation of instrumental variables models, as these models are useful for certain violations of “no unmeasured confounding” assumptions. Currently, Dr. Ogburn's main focus is on developing new methods for statistical and causal inference in the presence of interference (when one subject’s treatment may affect other subjects’ outcomes) and for social network data; both of these represent violations of assumptions of independence among observations.