Time: 9:00-10:00am, Jun. 7th, 2020, Sunday
Venue: Zoom ID：910 1882 3700, Password：197561
Spreker: Wenbin Lu, Professor, North Carolina State University
Abstract： 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), under the linear structure equation model. We further connect the structure learning approach with our proposed causal graphical model, by extending Yu et al. (2019)’s model with 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. We conclude that by locking Hubei down, China successfully reduced 49.7% of the daily new confirmed cases, about 84% of which was the indirect effect contributed via the migration of major cities outside Hubei.
Speaker’s Bio：Dr. Wenbin Lu is Professor of Statistics at North Carolina State University. He obtained his Ph.D. from Department of Statistics at Columbia University in 2003. His research interests include biostatistics, high-dimensional data analysis, statistical and machine learning methods for precision medicine, and network data analysis. He has published more than 90 papers in a variety of statistical journals, including Biometrika, Journal of the American Statistical Association, Journal of the Royal Statistical Society (Series B), Annals of Statistics, and Journal of Machine Learning Research. He has graduated 23 Ph.D. students. His research is partly funded by several grants from the National Institute of Health. He is an Associate Editor Biostatistics, Biometrics and Statistica Sinica, and a fellow of American Statistical Association.
