【专家简介】：姜蓓，加拿大阿尔伯塔大学数学与统计科学系副教授、博士生导师，统计学会主席委员会(COPSS)新晋领袖奖获得者。于2008年获得阿尔伯塔大学生物统计学硕士学位，2014年获得密歇根大学生物统计学博士学位。主要研究领域包括隐私数据分析、贝叶斯分层建模、多视图数据集成的联合建模等。相关研究成果被广泛应用于妇女健康、心理健康、神经学、生态学等领域。目前是JASA副主编(应用与案例研究)。现已在统计顶刊和人工智能顶会发表论文几十篇。
【报告摘要】：In healthcare and precision medicine, estimating optimal treatment strategies for right-censored data while ensuring fairness across ethnic subgroups is crucial but remains under-explored. The problem presents two key challenges: measuring heterogeneous treatment effects (HTE) under fairness constraints and dealing with censoring mechanisms. We propose a general framework for estimating HTE using nonparametric methods and integrating user-controllable fairness constraints to address these problems. Under mild regularization assumptions, our method is theoretically grounded, demonstrating the double robustness property of the HTE estimator. Using this framework, we demonstrate that optimal treatment strategies balance fairness and utility. Using extensive simulations and real-world data analysis, we uncovered the potential of this method to guide the selection of treatment methods that are equitable and effective.
【报告时间】：2025年04月28日 10:00-11:00
【报告地点】：崇真楼110
