报告人：汪军
报告地点：数学与统计学院415室
报告时间：2023年11月18日星期六09:30-10:30
邀请人：高巍
报告摘要：
Estimation of the treatment effect parameter is one of the crucial problems with clinical
trials for two or multiple treatments. The covariate-adaptive randomization methods are often applied to balance treatment assignments across prognostic factors in clinical trials, such as the minimization and stratified permuted blocks method. We propose the machine learning and bayesian machine learning estimator of average treatment effects under covariate-adaptive randomization methods. The proposed machine learning estimator is consistent and asymptotically normally distributed. Simulation studies show that the proposed machine learning and Bayesian machine learning estimators are comparable with Ye’s estimator, and it performs better than the Bugni’s estimator and unadjustment estimator when the outcome model is linear models. The proposed estimator has some advantages over the Bugni’s estimator and Ye’s estimator in terms of the standard error and root mean squared error when the outcome model is nonlinear models. The proposed method is robust for the form of outcome model. Finally, we apply the proposed methodology to a data set that studies the treatment effect of insurance provision on tobacco production for 12 tobacco producing counties in Jiangxi Province, China during 2003.
主讲人简介：
云南大学数学与统计学院副教授，2020年6月获得东北师范大学统计学专业博士学位，2022年11月云南大学统计学博士后流动站出站。主持和参与国家自然科学基金项目和国家社会科学基金项目多项，发表SCI论文多篇。主要从事处理效应、政策评估、面板数据、机器学习等方面的研究。
