时 间：2023年6月28日 10:00-11:00
地 点：理科大楼A1514
报告人：蔡亨瑞 加利福尼亚大学尔湾分校助理教授
主持人：章迎莹华东师范大学副教授
摘 要：
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. An analysis of causal effects that interprets the causal mechanism contributed through mediators is hence challenging but on demand. 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 in causal relation learning, heterogeneity and spuriousness. To characterize the heterogeneity, we first conceptualize heterogeneous causal graphs (HCGs) by generalizing the causal graphical model with confounder-based interactions and multiple mediators. In practice, only a small number of variables in the graph are relevant for the outcomes of interest. As a result, causal estimation with the full causal graph -- especially given limited data -- could lead to many falsely discovered, spurious variables that may be highly correlated with but have no causal impact on the target outcome. We 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 of simulated and real data applications, we show that the proposed algorithms outperform existing ones and can reveal true heterogeneous and non-spurious causal graphs.
报告人简介：
蔡亨瑞是加州大学欧文分校统计系助理教授，博士毕业于北卡罗莱纳州立大学。主要研究方向为因果推断，强化学习，图模型，精准决策等。研究成果发表于JMLR, ICML,NIPS, ICLR等二十余篇。