报告题目：Debiased machine learning for logistic partially linear mediation models with high-dimensional confounders

报告人：吴翊宁 博士生 南开大学统计与数据科学学院

报告时间：2026年5月11日，15：00---16：00

报告地点：伍卓群楼第二报告厅

校内联系人：朱复康 [email protected]

报告摘要：In this paper, we propose debiased machine learning strategies for estimating direct effect, indirect effect and total effect in logistic partially linear mediation models with high-dimensional confounders. To obtain asymptotically efficient estimators for the effects of interest, two Neyman-orthogonal score functions are proposed to remove regularization bias caused by the estimation of the nuisance functions. To address nonlinearity and unextractability of the logit link, double data splitting is applied to estimate nuisance functions and mitigate potential overfitting. Theoretically, we establish rigorous asymptotic properties for the proposed estimators of all three effects and derive their asymptotic normal distributions. The satisfactory performance of our proposed estimators is demonstrated by simulation results and a real-world PM2.5 concentration data from Beijing.

报告人简介：吴翊宁，2020-2024年本科就读于成人直播平台 统计学专业，现为南开大学统计学二年级博士生，研究方向主要为双机器学习、中介分析和预测后推断，论文发表于期刊Statistics and Computing。