数学与统计学院学术报告[2020] 080号

(高水平大学建设系列报告433号)

报告题目:  A Simple and Efficient Estimation of Average Treatment Effects in Models With Unmeasured Confounders

报告人：张政（中国人民大学统计与大数据研究院）

报告时间：2020年10月30日上午10:15-11:00　　　　　　　　　 　　　　　

报告地点： 腾讯会议 会议号码：701968444　　 　　　　　　　　　　　　　　　　　

报告内容： This paper presents a simple and efficient estimation of the average treatment effect (ATE) and local average treatment effect (LATE) in models with unmeasured confounders. In contrast with the existing studies which estimate some unknown functionals in the influence function either parametrically or semiparametrically, we do not model the influence function. Instead we apply the calibration method to a growing number of moment restrictions to estimate the weighting functions nonparametrically and then estimate ATE and LATE by plugging in. The calibration method is similar to the covariate-balancing method in that both methods exploit the moment restrictions. The difference is that the calibration method imposes the sample analogue of the moment restrictions, which is the key for efficient estimation of ATE and LATE. A simulation study reveals that our estimators have good finite sample performance and outperform the existing alternatives. An application to the empirical analysis of return to education illustrates the practical value of the proposed method.

报告人简历: 张政，中国人民大学统计与大数据研究院助理教授，香港中文大学博士。张政博士在JRSS-B, Stochastic Processes and their Applications, Statistica Sinica，Journal of Multivariate Analysis, Journal of Statistical Planning and Inference等国际知名学术期刊上发表过多篇文章。主要研究领域包括因果推断，缺失数据、非参数统计等。

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数学与统计学院

2020年10月28日