Hosted by the GPH Department of Biostatistics
Often in modern applications of causal inference, where the observed data either naturally emerge or may be summarized as distribution functions, the interest lies in the causal effect on the distributions themselves, rather than a summary measure such as the mean or the quantities. In this talk, Dr. Zhenhua Lin, Presidential Young Professor of statistics at the National University of Singapore, will introduce a framework of causal effects for outcomes from the Wasserstein space of distribution functions, which in contrast to the Euclidean space, is non-linear.
About the Speaker:
Zhenhua Lin, PhD is currently the Presidential Young Professor in Department of Statistics and Data Science, National University of Singapore. He received his PhD in statistics from University of Toronto, master's degrees in both statistics and computing science from Simon Fraser University, and bachelor's degree in computer science from Fudan University. His research primarily focuses on non-Euclidean/high-dimensional/functional data analysis and statistics under non-statistical constraints, with papers published in a number of journals including, The Annals of Statistics, Journal of the American Statistical Association, Biometrika, Journal of the Royal Statistical Society (Series B), Biometrics, Journal of Computational and Graphical Statistics. He serves as an associate editor for Bernoulli Journal.