题目：Tuning-parameter-free optimal propensity score matching approach for causal inference

主讲人：华东师范大学 刘玉坤教授

主持人：统计学院 常晋源教授

时间：2022年6月10日（周五）上午10:00-11:00

地点：腾讯会议，633 860 649

报告摘要：

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 for the univariate case, and achieves this level of efficiency in the multivariate case when 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.

主讲人简介：

刘玉坤，华东师范大学统计学院教授，博士生导师，统计交叉科学DB视讯(中国)副院长，入选国家高层次青年人才计划。本科和博士毕业于南开大学统计系。研究兴趣包括经验似然、半参数统计理论及其在缺失数据、因果推断、偏差数据、生态学、流行病学等方面的应用，在国内外重要统计期刊发表多篇科研论文。主持国家自然科学基金项目4项和科技部国家重点专项课题1项，参与重点项目2项；担任《应用概率统计》编委和责任编辑、《Statistical Theory and Related Fields》主编助理、以及《Journal of Applied Statistics》编委。