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.
Daniel Scharfstein is Professor of Biostatistics at the Johns Hopkins Bloomberg School of Public Health. He joined the faculty at Johns Hopkins in 1997, after doctoral and post-doctoral training in Biostatistics at the Harvard School of Public Health. He is a Fellow of the American Statistical Association. He received the 1999 Snedecor Award for best paper in Biometry and was recognized as the 2010 Distinguished Alumni Award from the Harvard Department of Biostatistics. His research is focused on how to draw inference about treatment effects in the presence of selection bias.