Colloquium - Ying Zhou
Institution: University of Toronto
Title: The Promises of Parallel Outcomes
Date: February 9, 2023
Location: C506 Wells Hall, Simulcast to Zoom (Click here for meeting details)
Time: 2:00 PM - 2:50 PM Eastern Time
Abstract:
A key challenge in causal inference from observational studies is the identification
and estimation of causal effects in the presence of unmeasured confounding. In this
talk, I will introduce a novel approach for causal inference that leverages information
in multiple outcomes to deal with unmeasured confounding. The key assumption in this
approach is conditional independence among multiple outcomes. In contrast to existing
proposals in the literature, the roles of multiple outcomes in the key identification
assumption are symmetric, hence the name parallel outcomes. I will show nonparametric
identifiability with at least three parallel outcomes and provide parametric estimation
tools under a set of linear structural equation models. The method is applied to a
data set from Alzheimer's Disease Neuroimaging Initiative to study the causal effects
of tau protein level on regional brain atrophies.
Bio:
Ying Zhou is a fifth-year Ph.D. student in Statistics at the University of Toronto.
She received her M.A. in Mathematics of Finance from Columbia University, and B.S.
in Mathematics and B.A. in Economics from Wuhan University. She has received the IMS
Hannan Graduate Student Travel Award in 2021. Her research interests focus on causal
inference and interdisciplinary data science.