Past talk · AI/ML Seminar Series
Towards Causal Revolution: On Learning Heterogeneity and Non-Spuriousness in Causal Graphs
Assistant Professor of Statistics, University of California, Irvine
- Date & time
- Monday, May 1, 2023 · 1:00 PM
- Location
- Donald Bren Hall 4011
Abstract
The causal revolution has spurred interest in understanding complex relationships in various fields. Under a general causal graph, the exposure may have a direct effect on the outcome and also an indirect effect regulated by a set of mediators. In this talk, we introduce a new statistical framework to comprehensively characterize causal effects with multiple mediators, namely ANalysis Of Causal Effects (ANOCE). Built upon such causal impact learning, we focus on two emerging challenges: heterogeneity and spuriousness. We conceptualize heterogeneous causal graphs (HCGs) and propose to learn a class of necessary and sufficient causal graphs (NSCG) that only contain causally relevant variables by utilizing the probabilities of causation. Across empirical studies, we show that the proposed algorithms outperform existing ones and can reveal true heterogeneous and non-spurious causal graphs.
About the speaker
Dr. Hengrui Cai is an Assistant Professor in the Department of Statistics at the University of California Irvine. She obtained her Ph.D. in Statistics at North Carolina State University in 2022. Her research focuses on causal inference and causal structure learning, and policy optimization and evaluation in reinforcement/deep learning.