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

报告时间：2022年4月1日上午 10 ：00

会议链接：https://meeting.tencent.com/dm/vEWkfGcD9J5h

会议 ID：481-573-681

主办单位：科研处/数学与统计学院

主讲人：童行伟

童行伟简介：北京师范大学教授，博士生导师，目前担任北京师范大学数理统计系系主任。博士毕业于北京大学数学科学学院，美国University of Missouri, Columbia 博士后，长期从事生物统计、金融统计、因果分析及稳健统计领域前沿研究。现担任中国概率统计学会的常务理事；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（International）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，和1项国家自然科学重点子课题、面上项目等6项，在Annals of Statistics, Biometrika, Statistica Sinica,等顶尖期刊发表50余篇 ，出版1本教材。

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.