Joint UCONN/UMASS STATISTICS COLLOQUIUM
Ted Westling, Assistant Professor
Department of Mathematics & Statistics
University of Massachusetts, Amherst
Causal Inference with Continuous Exposures
Abstract
Much of the literature on estimating causal effects concerns discrete exposures. Recently, there has been increased interest in continuous exposures; that is, exposures that can take an uncountable number of values. Examples of such exposures include air pollution, pre-vaccination antibody responses, and concentrations of harmful chemicals in the blood. In this talk, I will provide an introduction to the area of causal inference with continuous exposures. I will then provide an overview of some of the recent research concerning nonparametric causal inference with continuous exposures, including my own recent and ongoing research. In particular, I will discuss approaches to nonparametric pointwise and global inference on causal dose-response curves, and, time permitting, inference on alternative causal parameters such as the effects of stochastic and incremental interventions.
Date: Wednesday, October 7, 2020, 2:00 pm EST. Duration – 1 hour
For more information, contact: Tracy Burke at tracy.burke@uconn.edu