时 间：2022年10月31日 13:30-14:30
地 点：腾讯会议ID:599-643-036
报告人：朱慧晨 助理教授
主持人：唐炎林 研究员
摘 要：
In many applications, heterogeneous treatment effects on a censored response variable are of primary interest, and it is natural to evaluate the effects at different quantiles (e.g., median). The large number of potential effect modifiers, the unknown structure of the treatment effects, and the presence of right censoring pose significant challenges. We develop a hybrid forest approach called Hybrid Censored Quantile Regression Forest (HCQRF) to assess the heterogeneous effects varying with high-dimensional variables. The hybrid estimation approach takes advantage of the random forests and the censored quantile regression. We propose a doubly-weighted estimation procedure that consists of a weight to handle censoring and an adaptive weight derived to handle high-dimensional effect functions. We also propose a variable importance decomposition to measure the impact of a variable on the treatment effect function.
报告人简介：
朱慧晨，现任香港中文大学研究助理教授，曾任香港科技大学研究助理教授。2008-2012年复旦大学统计系本科，2012-2014年伊利诺伊大学香槟分校统计系硕士，2014-2019年哥伦比亚大学生物统计系博士，主要研究领域在分位数回归，机器学习等，在Biostatistics, Computational Statistics and Data Analysis, Journal of Multivariate Analysis等杂志发表论文。