Recent advances in statistical methodologies in evaluating program for high-dimensional data

作     者：ZHAN Ming-feng CAI Zong-wu FANG Ying LIN Ming ZHAN Ming-feng;CAI Zong-wu;FANG Ying;LIN Ming

作者机构：Wang Yanan Institute for Studies in Economics and Fujian Key Laboratory of Statistical SciencesXiamen UniversityXiamen 361005China Department of EconomicsUniversity of KansasLawrenceKS 66045USA Department of Statistics and Data ScienceXiamen UniversityXiamen 361005China 

出 版 物：《Applied Mathematics(A Journal of Chinese Universities)》 (高校应用数学学报（英文版）（B辑）)

年 卷 期：2022年第37卷第1期

页      面：131-146页

核心收录：

学科分类：02[经济学] 0202[经济学-应用经济学] 020208[经济学-统计学] 07[理学] 0714[理学-统计学（可授理学、经济学学位）] 070103[理学-概率论与数理统计] 0701[理学-数学] 

基　　金：Supported by the National Natural Science Foundation of China(71631004, 72033008) National Science Foundation for Distinguished Young Scholars(71625001) Science Foundation of Ministry of Education of China(19YJA910003) 

主　　题：causal inference covariate balance de-biased Lasso dimension reduction doubly robust high dimensions machine learning treatment effect 

摘      要：The era of big data brings opportunities and challenges to developing new statistical methods and models to evaluate social programs or economic policies or interventions. This paper provides a comprehensive review on some recent advances in statistical methodologies and models to evaluate programs with high-dimensional data. In particular, four kinds of methods for making valid statistical inferences for treatment effects in high dimensions are addressed. The first one is the so-called doubly robust type estimation, which models the outcome regression and propensity score functions simultaneously. The second one is the covariate balance method to construct the treatment effect estimators. The third one is the sufficient dimension reduction approach for causal inferences. The last one is the machine learning procedure directly or indirectly to make statistical inferences to treatment effect. In such a way, some of these methods and models are closely related to the de-biased Lasso type methods for the regression model with high dimensions in the statistical literature. Finally, some future research topics are also discussed.