Abstract: Research activities in observational studies have seen a recent surge owing to multiple factors. Among others, these factors include our ability and need to collect large amounts of data along with the development of powerful machine learning algorithms. Consequently, researchers have focused on tailoring this powerful machinery to answer questions in studies with both a large number of observations and potentially many covariates. Typical examples of such problems include, but are not limited to, estimating causal effects of administered treatments or understanding relevant parameters in missing data studies. However, this large array of research activity often comes with the burden of assumptions — the heart of which lies in justifying low bias for suggested statistical procedures. Driven by this need to reduce bias, in this talk, I will discuss a unified framework to obtain “optimal” inferential procedures for quantities of interest in common observational studies. This framework is based on extending classical semiparametric theory to understand necessary and sufficient conditions under which efficient inference is possible. Finally, proceeding through theoretical and numerical analyses, I will also try to discuss broader research questions attached to this paradigm and elaborate on its relevance in scientific research. This talk is based on several projects with multiple people, including—James Robins, Eric Tchetgen Tchetgen, Whitney Newey, Subhabrata Sen, Lingling Li, Lin Liu, and Aad Van der Vaart.
Zoom meeting: https://illinois.zoom.us/j/81872836690?pwd=OEVHTmtvdHBObXE1MmtmaUFqNldpUT09
Meeting ID: 818 7283 6690
Password: 089917
