Ted Westling : Causal Inference with Continuous Exposures

Posted by Kyunghee Han , part of the Statistics and Data Science Seminar.

At

Sept. 15, 2021, 4 p.m.

In

Zoom

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.