应数学与统计学院概率统计研究所邀请，美国加州大学河滨分校马舒洁教授将于2024年7月26日上午进行学术报告，欢迎全校师生参加。

报告题目：Causal inference on quantile dose-response functions via local ReLU least squares weighting

时  间：7月26日（星期五）上午8:30

地  点：理工楼631报告厅

报告摘要：In this talk, I will introduce a novel local ReLU network least squares weighting method to estimate quantile dose-response functions in observational studies. Unlike the conventional inverse propensity weighting (IPW) method, we estimate the weighting function involved in the treatment effect estimator directly through local ReLU least squares optimization. The proposed method takes advantage of ReLU networks applied for the baseline covariates with increasing dimension to alleviate the dimensionality problem while retaining flexibility and local kernel smoothing for the continuous treatment to precisely estimate the quantile dose-response function and prepare for statistical inference. Our method enjoys computational convenience, scalability, and flexibility. It also improves robustness and numerical stability compared to the conventional IPW method. We show that the ReLU networks can break the notorious `curse of dimensionality' when the weighting function belongs to a newly introduced smoothness class.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.

欢迎广大师生参加！

报告人简介

马舒洁教授，于2011年在美国密歇根州立大学(Michigan State University)获得统计学博士学位，现为美国加州大学河滨分校（University of California at Riverside）统计系教授、研究生项目主任。她是国际数理统计学会会士(IMS)和美国统计学会(ASA)会士(Fellow)、国际统计学会(ISI)推选会员(Elected Member)，现任Journal of the American Statistical Association, Journal of Business & Economic Statistics,Computational Statistics and Data Analysis,Journal of Machine Learning Research的副主编或编委。马教授的主要研究领域为：精准医疗、亚组分析、因果推断、大数据机器学习和深度学习、网络分析、非参数和半参数推理以及高维和纵向数据分析，现已在Annals of Statistics, Journal of the American Statistical Association, Journal of Econometrics, Journal of Machine Learning Research等统计学、计量经济、机器学习的国际知名期刊上发表50余篇论文。

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