深圳技术大学大数据与互联网学院数学系学术报告（六）

报告题目：Propensity Score-based Spline Approach for  Average Causal Effects

报告人：童行伟  教授（北京师范大学）

报告时间：2022年11月22日（周二） 16:00-17.00

报告地点：腾讯会议: 988-467-958

邀请人： 曹志强  博士

报告摘要：

When estimating the average causal effect in observational studies, researchers have to tackle both self-selection of treatment and outcome modeling. This is difficult since usually there are a large number of covariates that affect people's treatment decision and the true functional form in the model is not known. Propensity score is a popular approach for dimension reduction in causal inference. We propose a new semiparametric estimation strategy using B-spline based on the propensity score, which does not rely on parametric model specification. We further improve the efficiency of the estimator by addressing the error heteroscedasticity. We also establish the asymptotic properties of both estimators. The simulation studies show that our methods compare favorably with many competing estimators.  Our methods are advantageous over weighting estimators as it is not affected by extreme weights. We apply the proposed methods to data from the Ohio Medicaid Assessment Survey (OMAS) 2012, estimating the effect of having health insurance on self-reported health status for a population with subsidized insurance plan choices under the Affordable Care Act.

主讲人简介：

童行伟，北京师范大学统计学院教授，博士生导师。主要从事生物统计，金融统计等方向的研究。现担任中国概率统计学会常务理事；中国现场统计研究会常务理事；高维数据统计分会秘书长； 《应用概率统计》杂志的编委；资源与环境统计分会常务理事；国际生物统计学会中国分会常务理事，北京大数据协会副会长等；主持科技部重点研发课题1项，国家自然科学重点子课题1项、面上项目等6项，在Annals of Statistics, Biometrika, Biometrics等顶尖期刊发表论文50余篇，出版教材1本。