统计与数据科学系系列学术报告之四百一十一期

时   间：2023年11月7日（周二）14:00-15:00

地   点：李达三楼105室

主持人：复旦大学 管理学院 统计与数据科学系 郁文 教授

报告人：刘玉坤  教授    华东师范大学  统计学院

题   目：Tuning-parameter-free propensity score matching approach for causal inference under shape restriction

摘   要：Propensity score matching (PSM) is a pseudo-experimental method that uses statistical techniques to construct an artificial control group by matching each treated unit with one or more untreated units of similar characteristics. To date, the problem of determining the optimal number of matches per unit, which plays an important role in PSM, has not been adequately addressed. We propose a tuning-parameter-free PSM method based on the nonparametric maximum-likelihood estimation of the propensity score under the monotonicity constraint. The estimated propensity score is piecewise constant, and therefore automatically groups data. Hence, our proposal is free of tuning parameters. The proposed estimator is asymptotically semiparametric efficient when the covariate is univariate or the outcome and the propensity score depend on the covariate in the same direction. We conclude that matching methods based on the propensity score alone cannot, in general, be efficient.

个人简介：刘玉坤，华东师范大学统计学院教授，博士生导师，入选教育部“**学者奖励计划”青年学者。本科和博士毕业于南开大学统计系，之后一直在华东师范大学任教。研究兴趣包括经验似然和半参数统计理论、缺失数据分析、因果推断及统计交叉应用。研究成果曾发表在JRSSB、AOS、JASA、Biometrika、Biometrics等国际统计学期刊; 主持科技部国家重点研发计划课题和多项国家自然科学基金项目。

统计与数据科学系

2023-11-01