报 告 题 目：An Alternative Doubly Robust Estimation in Causal Inference Model

主 讲 人：李 高 荣

单 位：北京师范大学

时 间：11月7日15:40

腾讯 ID: 382-752-412

摘 要：

Doubly robust (DR) methods that employ both the propensity score and outcome models are widely used to estimate the causal effect of a treatment and generally outperform those methods only using the propensity score or the outcome model. However, without appropriately chosen the working models, DR estimators may substantially lose efficiency. In this paper, based on the augmented inverse probability weighting procedure, we derive a new estimating equation for the causal effect by the strategy of combining estimating equations. The resulting estimator by solving the new estimating equation retains doubly robust and can improve the efficiency under the misspecification of conditional mean working model. We further show the large sample properties of the proposed estimator under some regularity conditions. Through simulation experiments and a real data analysis, we illustrate that the proposed method is competitive with its competitors, which is in line with those implied by the asymptotic theory.  

简 介：

李高荣，北京师范大学统计学院教授，博士生导师，北京师范大学第十二届“最受本科生欢迎的十佳教师”。全国工业统计学教学研究会常务理事、中国现场统计研究会第十一届理事、中国概率统计学会第十一届理事、中国工业互联网研究院技术专家委员会专家。主要研究方向是非参数统计、高维统计、统计学习、纵向数据、测量误差数据和因果推断等。迄今为止，在Annals of Statistics, Journal of the American Statistical Association, Journal of Business & Economic Statistics, Statistics and Computing, 《中国科学：数学》和《统计研究》等学术期刊上发表学术论文100余篇。在科学出版社出版3部著作：《纵向数据半参数模型》、《现代测量误差模型》(入选“现代数学基础丛书”系列)和《多元统计分析》(入选“统计与数据科学丛书”系列)。入选北京市属高等学校人才强教深化计划“中青年骨干人才培养计划”，北京市优秀人才培养资助计划和北京工业大学“京华人才”支持计划。主持国家自然科学基金、北京市自然科学基金和北京市教委科技计划面上项目等国家和省部级科研项目10多项。