Propensity Score-based Spline Approach for Average Causal Effects
主讲人: 童行伟

童行伟 北京师范大学统计学院数理统计系系 教授
1989-1993 本科 吉林大学
1993-1996 硕士 北京大学
2000-2003 博士 北京大学
2005-2006 博士后 密苏里大学,哥伦比亚

主持人: 钟威

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.

时间: 2018-12-18(Tuesday)16:40-18:00
地点: D236
主办单位: 统计系
类型: 厦门大学高级计量经济学与统计学系列讲座