【专家简介】：曾冬林教授于2001年获密西根大学统计博士，之后在北卡大学任教授到2023 年，2023年后在密西根大学生物统计系当教授至今。研究方向包括semiparametric inference, machine learning, causal inference and dynamic treatment regime。曾教授2010年当选国际数理统计学会（IMS）的会士，2011年当选美国统计学会（ASA）会士。
【报告摘要】：Data driven individualized decision making problems have received a lot of attentions in recent years. In particular, decisionmakers aim to determine the optimal Individualized Treatment Rule (ITR) so that the expected specified outcome averaging over heterogeneous patient-specific characteristics is maximized. Many existing methods deal with binary or a moderate number of treatment arms and may not take potential treatment effect structure into account. However, the effectiveness of these methods may deteriorate when the number of treatment arms becomes large. In this article, we propose GRoup Outcome Weighted Learning (GROWL) to estimate the latent structure in the treatment space and the op-timal group-structured ITRs through a single optimization. In particular, for estimating group-structured ITRs, we utilize the Reinforced Angle based Multicategory Support Vector Machines (RAMSVM) to learn group-based decision rules under the weighted angle based multi-class classification framework. Fisher consistency, the excess risk bound, and the convergence rate of the value function are established to provide a theoretical guaran-tee for GROWL. Extensive empirical results in simulation studies and real data analysis demonstrate that GROWL enjoys better performance than several other existing methods.
【报告时间】：2025年07月09日9:20-10:00
【报告地点】：崇真楼110
