报告题目：Estimating optimal treatment regimes in semi-supervised framework
报 告 人：彭梦姣 助理教授 华东师范大学
报告时间：2022年7月20日 14：00-15：00
报告地点：腾讯会议 ID：999253931 会议密码：2022
校内联系人：王培洁 wangpeijie@jlu.edu.cn
报告摘要：Finding the optimal individualized treatment rule mapping from the individual characteristics or contextual information to the treatment assignment has been studied intensively in the literature, with important applications in practice. We consider the problem of estimating the optimal treatment regime in a semi-supervised learning setting, where a very small proportion of the entire set of observations are labeled with the true outcome but features predictive of the outcome are available among all observations. We propose a model-free robust inference approach for optimal treatment regime by the aid of the unlabeled data with only covariate information to improve estimation efficiency. The proposed estimation of OPT primarily involves a flexible nonparametric imputation by single index kernel smoothing which works well even for high-dimensional covariates; and a follow-up estimation for optimal treatment regime based on concordance-assisted learning, including optimization of the estimated concordance function up to a threshold and finding the optimal threshold to maximize the inverse propensity score weighted (IPSW) estimator of the value function. Moreover, when the propensity score function is unknown, a doubly robust estimation method is developed under a class of monotonic index models. Our estimators are shown to be consistent and asymptotically normal. Simulations exhibit the efficiency and robustness of the proposed method compared to existing approaches in finite samples.
报告人简介：彭梦姣，毕业于新加坡南洋理工大学，获统计学专业理学博士学位，以及新加坡数学协会数学科学方向最佳博士毕业论文奖。2020年9月入职华东师范大学统计交叉科学研究院，任职助理教授，从事生存分析、复杂数据分析与建模，高维统计、统计机器学习等研究，在Statistical Methods in Medical Research, Computational Statistics Data Analysis (CSDA), Expert Systems with Applications等国际期刊发表论文多篇。
