Statistics and Data Science Seminar: Sparse Causal Learning: Challenges and Opportunities

Abstract: There has been a recent surge in attention towards trustworthy AI, especially as it starts playing a pivotal role in high-stakes domains such as healthcare, the justice system, and finance. Causal inference emerges as a promising path toward building AI systems that are stable, fair, and explainable. However, it often hinges on precise and strong assumptions. In this talk, I introduce sparse causal learning as a common ground between trustworthy AI and robust causal inference. Specifically, I reconsider the supervised learning problem of predicting an outcome using multiple predictors through the lens of causality. I show that it is possible to remove spurious correlations caused by unmeasured confounding by leveraging low-dimensional structures in the predictors. This new approach leads to algorithms that are theoretically justifiable, computationally feasible, and statistically sound.

Bio: Linbo Wang is an assistant professor in the Department of Statistical Sciences and the Department of Computer and Mathematical Sciences, University of Toronto. He is also a faculty affiliate at the Vector Institute, a CANSSI Ontario STAGE program mentor,  and holds affiliate assistant professor positions in the Department of Statistics at the University of Washington and the Department of Computer Science at the University of Toronto. Prior to these roles, he was a postdoc at Harvard T.H. Chan School of Public Health. He obtained his Ph.D. from the University of Washington. His research focuses on causality and its interaction with statistics and machine learning.

Host: Jimin Ding