学术报告：Causal Inference on Quantile Dose-response Functions via Local ReLU Least Squares Weighting

报告时间：11月18日（星期一）上午10:30-11:30

报告地点：沙河校区，学院1号楼102会议室

报告人：张政，中国人民大学，副教授

报告摘要：This paper proposes 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. For the ReLU network approximation, we introduce a mixed fractional Solbolev ball class and show that the two-layer ReLU networks can relieve the `curse of dimensioanlity' when the weighting function belongs to this function 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.

报告人简介：张政，中国人民大学统计与大数据研究院长聘副教授、博士生导师，国家级青年人才计划入选者。2011年本科毕业于东南大学数学系，2015年博士毕业于香港中文大学统计系。主要研究方向为因果推断。在JRSS-B, JOE, Quantitative Economics等期刊发表多篇学术论文。担任中国现场统计研究会统计交叉科学研究分会常务理事、因果推断分会理事。

撰稿人：刘洁

审稿人：邓露