Topic: “Estimands and Causality”
Prof. Dr. Daniel Scharfstein is a full professor in the Department of Biostatistics at the Johns Hopkins Bloomberg School of Public Health since 2008. His internationally acclaimed research focuses on statistical methods of causal inference, especially in the context of selection bias. He has made important contributions to the question of how to deal with informative missing values or censorship in RCTs and to the statistical analysis of observational studies with non-randomized treatment. His more than 100 mostly methodological publications have appeared in high-ranking journals such as JRSS, JASA, Biometrika, Statistics in Medicine. He was a member of the National Academy Committee that wrote the recommendation “The Prevention and Treatment of Missing Data in Clinical Trials”. He has also been and continues to be the lead statistician in a number of evaluation studies, e.g. the National Study of the Costs and Outcomes of Trauma (NSCOT), Guided Care for Chronically Ill Older Adults and Healthy Steps for Young Children. He has made important contributions to the debate on the ICH E9 Addendum on “Estimands”, e.g. in the journal “Clinical Trials”.
Topic: “Machine Learning in Biometry”
Prof. Chris Holmes, Department of Statistics, University of Oxford.