主题：Heterogeneous Quantile Treatment Effect Inference for Longitudinal Data with High-Dimensional Confounding
主讲人：华东师范大学统计学院统计学系主任唐炎林教授
主持人：统计与数据科学学院周岭教授
时间：2026年5月23日（周六）上午10：30-11：20
地点：柳林校区弘远楼408会议室
主办单位：统计与数据科学学院和统计研究中心 科研处
主讲人简介：
唐炎林，华东师范大学统计学院教授，博士生导师，统计学系主任；入选国家级人才计划、上海市浦江人才计划。主要研究方向为分位数回归、共形预测、高维异质性数据统计推断，主持多项国家自然科学基金、上海市自然科学基金，担任SCI期刊Statistica Sinica、Journal of the Korean Statistical Society的编委。在Biometrika、JRSSB、PNAS、Biometrics等发表论文近50篇。
内容提要：
Causal inference plays a fundamental role in various real-world applications. However, in the motivating non-small cell lung cancer (NSCLC) study, it is challenging to estimate the treatment effect of chemotherapy on circulating tumor DNA (ctDNA). First, the heterogeneous treatment effects vary across patient subgroups defined by baseline characteristics. Second, there exists a broad set of demographic, clinical and molecular variables act as potential confounders. Third, ctDNA trajectories over time show heavy-tailed non-Gaussian behavior. Finally, repeated measurements within subjects introduce unknown correlation. Combining convolution-smoothed quantile regression and orthogonal random forest, we propose an estimation and inference framework for heterogeneous quantile treatment effects in the presence of high-dimensional confounding, which not only captures effect heterogeneity across covariates, but also behaves robustly to nuisance parameter estimation error. We establish the theoretical properties of the proposed estimator and demonstrate its finite-sample performance through comprehensive simulations. We illustrate its practical utility in the motivated NSCLC study.
