Data Science Seminar
Semiparametrics: A Biostatistician’s Toolbox
Daniel Scharfstein
Semiparametrics: A Biostatistician’s Toolbox
| When | Friday, October 30, 2020, 11:50 AM – 1:10 PM (MT) |
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Abstract
In this talk, I will discuss the theory of semiparametrics that I use to estimate causal effects at root-n rates. Estimators of these effects depend on estimators of nuisance parameters that can be estimated at rates slower than root-n; I provide sufficient conditions for these rates. I will seek advice on the machine learning estimation techniques that satisfy these conditions. I will illustrate the theory in the context of estimating the causal contrast of two competing treatments based on data from a comprehensive cohort study in which clinically eligible individuals are first asked to enroll in a randomized trial and, if they refuse, are then asked to enroll in a parallel observational study in which they can choose treatment according to their own preference.
Speaker
Daniel Scharfstein
Utah, Population Health Sciences
Daniel Scharfstein is a Professor of Biostatistics in the Department of Population Health Sciences, at the University of Utah School of Medicine. He joined the U in August 2020 after spending 23 years on the faculty in the Department of Biostatistics at the Johns Hopkins Bloomberg School of Public Health.
Tags: causal inference health & medicine statistics
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