报告题目： A Data Fusion Method for Quantile Treatment Effects
报告摘要：With the increasing availability of datasets, developing data fusion methods to leverage the strengths of different types of data to draw causal effects is of great practical importance to many scientific fields. In this paper, we consider estimating the quantile treatment effects with small validation data with fully-observed confounders and large auxiliary data with unmeasured confounders. We propose a fused quantile treatment effects estimator (FQTE) by integrating the information from two datasets based on doubly robust estimating functions. We allow for the misspecification of the models on the dataset with unmeasured confounders. Under mild conditions, we show that the proposed FQTE is asymptotically normal and more efficient than the initial QTE estimator using the validation data solely. By establishing the asymptotic linear forms of related estimators, convenient methods for covariance estimation are provided to make our method easy to implement. Simulation studies demonstrate the empirical validity and improved efficiency of our fused estimators. We illustrate the proposed method with an application.
报告时间：2024年5月22日(周三)上午9点-10点
报告地点：启智楼80602会议室
报告人简介： 朱仲义，复旦大学统计与数据科学系教授，博士研究生导师；曾任中国概率统计学会第八、九届副理事长,国际著名杂志“Statistica Sinica”副主编; “应用概率统计”, “中国科学：数学”杂志编委；现为国际数理统计学会当选会员，担任“数理统计与管理”杂志编委和国际顶级统计杂志JASA的副主编。专业研究方向为：纵向数据(面板数据)模型；分位数回归模型，机器学习等。主持完成国家自然科学基金六项、国家社会科学基金一项，作为子项目负责人完成国家自然科学基金重点项目二项，重大项目子项目一项，目前主持国家自然科学基金面上，重点项目各一项。近几年发表论文100多篇(其中包括在国际四大统计和机器学习顶级刊物等SCI论文八十多篇)。获得教育部自然科学二等奖一次。
