Title: Towards Explainable Causal Revolution: Analysis of Causal Effects

 

In the era of causal revolution, identifying the causal effect of an exposure on the outcome of interest is an important problem in many areas, such as epidemics, medicine, genetics, and economics. 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. To the best of our knowledge, there are no feasible algorithms that give an exact decomposition of the indirect effect on the level of individual mediators, due to common interaction among mediators in the complex graph. In this paper, we establish a new statistical framework to comprehensively characterize causal effects with multiple mediators, namely, ANalysis Of Causal Effects (ANOCE), with a newly introduced definition of the mediator effect, under the linear structural equation model. We further propose a constrained causal structure learning method by incorporating a novel identification constraint that specifies the temporal causal relationship of variables. The proposed algorithm is applied to investigate the causal effects of 2020 Hubei lockdowns on reducing the spread of the coronavirus in Chinese major cities out of Hubei.

 

Dr. Hengrui Cai is an Assistant Professor in the Department of Statistics at University of California Irvine. She obtained her Ph.D. degree in Statistics at North Carolina State University in 2022. Cai has broad research interests in methodology and theory in causal inference, reinforcement learning and graphical modeling, as she works to establish reliable, powerful, and interpretable solutions to real-world problems. Currently, her research focuses on individualized optimal decision making with complex data; policy evaluation in reinforcement learning and deep learning; and causal discovery for high-dimensional individual mediation analysis, with a variety of applications in precision medicine, customized economics, personalized marketing, and modern epidemiology. Her work has been published in conferences including Conference on Neural Information Processing Systems (NeurIPS), International Conference on Machine Learning (ICML), International Conference on Learning Representations (ICLR), and International Joint Conference on Artificial Intelligence (IJCAI), as well as journals including Stat, and Statistics in Medicine.