Speaker: Dr. Linbo Wang, Associate Professor, Department of Statistical Sciences and the Department of Computer and Mathematical Sciences, University of Toronto

Title: 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.