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 Associate 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.

He is generally interested in understanding broad aspects of causal inference in observational studies in modern data settings with a focus on learning about fundamental challenges in the statistical analysis of environmental mixtures and their effects on the cognitive development of children and cogntitive decline in aging populations. His research is also motivated by learning through applications in large-scale genetic association studies, developing statistical methods to quantify the effects of climate change on human health, and understanding the effects of homelessness on human health.