報告題目：Estimating optimal treatment regimes in semi-supervised framework
報 告 人：彭夢姣 助理教授 華東師範大學
報告時間：2022年7月20日 14：00-15：00
報告地點：騰訊會議 ID：999253931 會議密碼：2022
校内聯系人：王培潔 wangpeijie@jlu.edu.cn
報告摘要：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.
報告人簡介：彭夢姣，畢業于新加坡南洋理工大學，獲統計學專業理學博士學位，以及新加坡數學協會數學科學方向最佳博士畢業論文獎。2020年9月入職華東師範大學統計交叉科學研究院，任職助理教授，從事生存分析、複雜數據分析與建模，高維統計、統計機器學習等研究，在Statistical Methods in Medical Research, Computational Statistics Data Analysis (CSDA), Expert Systems with Applications等國際期刊發表論文多篇。
