报告题目：Optimal Causative Inference via MMSPE
主讲嘉宾：Professor Zhengjun Zhang, a tenured professor and Chair of the Department of Statistics and Data Science at the School of Economics and Management, University of Chinese Academy of Sciences. He also serves as the Deputy Director of the Center for Forecasting Science at the Chinese Academy of Sciences. Prior to his current role, Professor Zhang was a tenured professor and Associate Chair in the Department of Statistics at the University of Wisconsin–Madison, and a joint faculty member in the Department of Biomedical Informatics there.Professor Zhang is a Fellow of both the Institute of Mathematical Statistics (IMS) and the American Statistical Association (ASA). He served as an Executive Committee Member and Treasurer of the IMS from July 2016 to July 2022. He is currently an Associate Editor for several leading international journals, including Journal of the American Statistical Association (JASA), Journal of Business & Economic Statistics (JBES), Statistica Sinica, Journal of Data Science (JDS), Electronic Journal of Statistics (EJS), and Statistical Theory and Related Fields (STaRF).
报告摘要：Understanding causal relationships among variables is crucial in economic, biological, medical, climate, and many other applied research fields. Conventional methods often struggle with asymmetric causality and high-dimensional data. To address these challenges from a machine learning perspective, this talk introduces MMSPE-HMAC—a Minimum Mean Squared Prediction Error (MMSPE) Hamiltonian-clustering Modernized Asymmetric Causality (HMAC) method. MMSPE-HMAC integrates Generalized Measures of Correlation (GMC) into deep clustering with a RadViz-style representation, utilizing an optimal Hamiltonian cycle to map clusters, similarities, and outliers. This enables clear visualization of causal relationships, offering a significantly different representation from existing approaches. Under the MMSPE principle, we theoretically justify that GMC leads to an optimal causative method. Compared to other causal inference techniques, MMSPE-HMAC requires the fewest structural and statistical assumptions. It is widely applicable, easily implementable, and empirically interpretable. Extensive experiments across synthetic, engineering, machine learning, economic, and financial datasets demonstrate MMSPE-HMAC's superior performance over existing methods. Importantly, MMSPE-HMAC leads to the correct direction of the simplest yet the hardest and unsolved causal inference problem in normality in the literature. Notably, MMSPE-HMAC reveals that USD/CNY exchange rate changes drive movements in USD/EUR, USD/GBP, and USD/JPY, while also identifying annual block timing effects in macroeconomic indicators. Furthermore, MMSPE-HMAC uncovers indirect causal effects in MNIST and fashion designs—patterns that are difficult to detect using other causal methods. Joint work with Tianyi Huang and Shenghui Cheng.
报告时间：2025年9月30日(周二)，16:30-18:00
线下地点：厦门大学经济楼C108
腾讯会议：146 172 952
