Abstract: In this talk, we consider the recently proposed framework of proximal causal inference. Nonparametric identification and semiparametric theory for the average treatment effect will be introduced. We will also discuss learning heterogeneous treatment effects, optimal individualized decision-making, and survival analysis under proximal causal inference framework.

About the Speaker:

崔逸凡，研究员，博士生导师。2018年于北卡罗来纳大学教堂山分校获得统计与运筹专业博士学位，曾在宾夕法尼亚大学沃顿商学院从事博士后研究工作。 回国前任职于新加坡国立大学统计与数据科学系担任助理教授，国家级青年人才计划入选者。当选ISI（国际统计学会）Elected Member，现担任Biometrical Journal的Associate Editor以及Journal of Machine Learning Research的editorial board reviewer。