Ying Zhou
Department of Statistics
University of Toronto
The Promises of Parallel Outcomes
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.
DATE: Tuesday, 1/24/23
TIME: 3:30 PM
PLACE: AUST 108
Webex link: https://uconn-cmr.webex.com/uconn-cmr/j.php?MTID=m0ce1e33d667aa0ded0d11bd5af8ef441
Coffee will be served at 3:00 pm in the Noether Lounge (AUST 326)
For more information, contact: Tracy Burke at tracy.burke@uconn.edu