唐炎林教授学术报告-申请方
主办方
江苏师范大学数学与统计学院
讲座介绍
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.
演讲人
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；入选国家青年高层次人才计划、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 40 余篇。
扫描二维码
下载申请方APP
下载申请方APP
扫描二维码
下载申请方APP