报告题目：Causal Inference on Quantile Dose-response Functions via Local ReLU Least Squares Weighting
报告人：马舒洁教授 加州大学河滨分校
报告时间：2024年7月24日（周三），上午10:00
报告地点：兴庆校区数学楼2-2会议室
报告摘要：In this talk, I will introduce a new local ReLU network least squares weighting method to estimate quantile dose-response functions in observational studies. Unlike the conventional inverse propensity weight (IPW) method, we estimate the weighting function involved in the treatment effect estimator directly through local ReLU least squares optimization. The method takes advantage of ReLU networks applied on the multivariate baseline covariates to alleviate the dimensionality problem while retaining flexibility and local kernel smoothing for the continuous treatment to achieve a precise estimation of the dose-response function and prepare for statistical inference. Our method enjoys computational convenience and scalability. It also improves robustness and numerical stability compared to the conventional IPW method. We also establish the convergence rate for the ReLU network estimator and the asymptotic normality of the proposed estimator for the quantile dose-response function. We further propose a multiplier bootstrap method to construct confidence bands for quantile dose-response functions. The finite sample performance of our proposed method is illustrated through simulations and a real data application.
个人简介：Dr. Shujie Ma is a Professor of Statistics at the University of California- Riverside. Her primary research interests include causal inference, precision medicine, machine learning and deep learning for big data, network data analysis and semiparametric inference. She is an elected fellow of ASA and IMS and an elected member of ISI. She is serving on the editorial boards of several journals, including the Journal of the American Statistical Association, Journal of Business and Economic Statistics, etc.
