On Statistical Inference in Observational Studies

Abstract: In this talk, we will focus on drawing inferences for average treatment effect type quantities arising in the context of many observational studies. In the first part of the talk, we will try to understand the problem's subtleties in low-dimensional nonparametric settings and discuss the potential usefulness of higher-order semiparametric theory to paint a detailed picture. In another half of the talk, we will consider high-dimensional aspects of the question and discuss different regimes and associated subtleties that arise due to many confounders. The focus of the second half of the problem will be to go beyond the regimes of the sparsity or low dimensional regularity that are traditionally assumed in the literature.  

 

 

Bio: Rajarshi Mukherjee is an Assistant Professor in the Department of Biostatistics at Harvard T.H. Chan School of Public Health. Previously, he was an Assistant Professor in the Division of Biostatistics at UC Berkeley following his time as a Stein Fellow in the Department of Statistics at Stanford University. He obtained his PhD in Biostatistics from Harvard University advised by Prof. Xihong Lin.