Betsy Ogburn, “Disentangling confounding and nonsense associations due to dependence”
Betsy Ogburn, PhD
Associate Professor, Department of Biostatistics
Johns Hopkins Bloomberg School of Public Health
Title: “Disentangling confounding and nonsense associations due to dependence”
Abstract: 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 two areas: causal inference with multiple treatments and unmeasured confounding and causal and statistical inference with social network data. For each of these areas I will demonstrate the flaws in existing methods and describe new methods that were inspired by careful consideration of dependence and confounding.
Biography: Betsy Ogburn is Associate Professor of Biostatistics at Johns Hopkins Bloomberg School of Public Health. Currently much of her research focuses on causal inference in the presence of unmeasured confounding, causal and statistical inference using data with complex dependence, and the efficient use of randomized trial data to find effective COVID treatments. Betsy completed her Ph.D. in Biostatistics at Harvard University and is a 2016 National Academy of Science Kavli Fellow.
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Meeting ID: 995 6750 4456
Passcode: Clark
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