Speaker: Assistant Professor Mauricio Sadinle, Department of Biostatistics, School of Public Health, University of Washington
Title: Nonparametric Identified Methods to Handle Nonignorable Missing Data
Abstract: There has recently been a lot of interest in developing approaches to handle missing data that go beyond the traditional assumptions of the missing data being missing at random and the nonresponse mechanism being ignorable. Of particular interest are approaches that have the property of being nonparametric identified, because these approaches do not impose parametric restrictions on the observed-data distribution (what we can estimate from the observed data) while allowing the estimation of a full-data distribution (what we would ideally want to estimate). When comparing inferences obtained from different nonparametric identified approaches, we can be sure that any discrepancies are the result of the different identifying assumptions imposed on the parts of the full-data distribution that cannot be estimated from the observed data, and consequently these approaches are especially useful for sensitivity analysis. In this talk I will present some recent developments in this area of research and discuss current challenges.