报告题目：Causal effect of functional treatment

时间：2023年4月4号 10:30-12:00

地点：数学院135

摘要：

Functional data often arise in the areas where the causal treatment effect is of interest. However, research concerning the effect of a functional variable on an outcome is typically restricted to exploring the association rather than the casual relationship. The generalized propensity score, often used to calibrate the selection bias, is not directly applicable to a functional treatment variable due to a lack of definition of probability density function for functional data. Based on the functional linear model for the average dose-response functional, we propose three estimators, namely, the functional stabilized weight estimator, the outcome regression estimator and the doubly robust estimator, each of which has its own merits. We study their theoretical properties, which are corroborated through extensive numerical experiments. A real data application on electroencephalography data and disease severity demonstrates the practical value of our methods.

报告人简介：

谭若虚博士，博士毕业于墨尔本大学数学与统计学院，师从Aurore Delaigle教授。他现在为香港大学统计与精算学院博士后，合作导师为尹国圣教授。他的研究兴趣包括：因果推断，缺失数据分析，函数型数据分析，流形学习和流形数据分析。他的研究成果发表在Statistica Sinica和Statistics in Medicine等国际学术期刊上。