Statistics Department Seminar Series: Elizabeth Ogburn, Associate Professor, Department of Biostatistics, Johns Hopkins University

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 will describe how confusion about these two phenomena results in shortcomings in popular methods in three areas: causal and statistical inference with social network data; causal inference with multiple treatments and unmeasured confounding; and causal inference with spatial data. For each of these three areas I will demonstrate shortcomings of existing methods and describe new methods that were inspired by careful consideration of dependence and confounding.


Betsy Ogburn is an Associate Professor in the Department of Biostatistics at Johns Hopkins University and founder of the COVID-19 Collaboration Platform.

https://www.eogburn.com/