Seminar by Dr. Linbo WANG from Department of Statistical Sciences, University of Toronto

In many observational studies, researchers are interested in studying the effects of multiple exposures on the same outcome. Unmeasured confounding is a key challenge in these studies as it may bias the causal effect estimate. To mitigate the confounding bias, we introduce a novel device, called the synthetic instrument, to leverage the information contained in multiple exposures for causal effect identification and estimation. We show that under linear structural equation models, the problem of causal effect estimation can be formulated as an ℓ₀-penalization problem, and hence can be solved efficiently using off-the-shelf software. Simulations show that our approach outperforms state-of-art methods in both low-dimensional and high-dimensional settings. We further illustrate our method using a mouse obesity dataset.

Dr. Linbo Wang is an Assistant Professor in the Department of Statistical Sciences of University of Toronto. He obtained PhD from University of Washington and served as postdoctoral fellow in Harvard T.H. Chan School of Public Health. His current research interests include Causal inference with unmeasured confounding, Variable selection in causal inference, Parameterization of discrete graphical models and Causal inference and optimal transport. He was awarded with the NSERC Discovery Accelerator Supplement (DAS) from the Natural Sciences and Engineering Research Council.

Dr. Wang is currently co-organizing the 23rd Meeting of New Researchers in Statistics and Probability (NRC) to be held during Aug 2 - 5, 2023 in Toronto, Canada.

Homepage: https://sites.google.com/site/linbowangpku/