报告题目：Regression adjustment for treatment effect with multicollinearity in high dimension

报告时间：2021年5月16日 下午 4:30-6:30

报告地点：科学会堂 602 教室

Abstract:

Randomized experiment is an important tool for studying the Average Treatment Effect (ATE). This paper considers the regression adjustment estimation of the Sample Average Treatment Effect (SATE) in high-dimensional case, where the multicollinearity problem is often encountered and needs to be properly handled. Many existing regression adjustment methods fail to achieve satisfactory performances. To solve this issue, an Elastic-net adjusted estimator for SATE is proposed under the Rubin causal model of randomized experiments with multicollinearity in high dimensions. The asymptotic properties of the proposed SATE estimator are shown under some regularity conditions, and the asymptotic variance is proved to be not greater than that of the unadjusted estimator. Furthermore, Neyman-type conservative estimators for the asymptotic variance are proposed, which yields tighter confidence intervals than both the unadjusted and the Lasso-based adjusted estimators. Some simulation studies are carried out to show that the Elastic-net adjusted method is better in addressing collinearity problem than the existing methods. The advantages of our proposed method are also shown in analyzing the dataset of HER2 breast cancer patients.

个人简介：李高荣，北京师范大学统计学院教授，博士生导师。全国工业统计学教学研究会常务理事、中国概率统计学会第十一届理事和中国工业互联网研究院技术专家委员会专家等。主要研究方向是非参数统计、高维统计、统计学习、纵向数据、测量误差数据和因果推断等。迄今为止，在Annals of Statistics, Journal of the American Statistical Association, Statistics and Computing, Statistica Sinica，中国科学：数学，和统计研究等学术期刊上发表学术论文90余篇。在科学出版社出版专著《纵向数据半参数模型》和《现代测量误差模型》。主持国家自然科学基金、北京市自然科学基金和北京市教委科技计划面上项目等国家和省部级科研项目10余项。