Learning Causal Effect Using Machine Learning with Application to China's Typhoon

作     者：Peng WU Qi-rui HU Xing-wei TONG Min WU Peng WU;Qi-rui HU;Xing-wei TONG;Min WU

作者机构：School of StatisticsBeijing Normal UniversityBeijing 100875China School of mathematics and StatisticsHubei University of Science and TechnologyHubei 437000China 

出 版 物：《Acta Mathematicae Applicatae Sinica》 (应用数学学报（英文版）)

年 卷 期：2020年第36卷第3期

页      面：702-713页

核心收录：

学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 07[理学] 081104[工学-模式识别与智能系统] 08[工学] 070601[理学-气象学] 0706[理学-大气科学] 0835[工学-软件工程] 0701[理学-数学] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：supported by the National Key Research and Development Program of China Grant 2017YFA0604903 National Natural Science Foundation of China Grant(Nos.11671338,11971064) 

主　　题：causal effect matching machine learning 

摘      要：Matching is a routinely used technique to balance covariates and thereby alleviate confounding bias in causal inference with observational *** of the matching literatures involve the estimating of propensity score with parametric model,which heavily depends on the model *** this paper,we employ machine learning and matching techniques to learn the average causal *** comparing a variety of machine learning methods in terms of propensity score under extensive scenarios,we find that the ensemble methods,especially generalized random forests,perform favorably with *** apply all the methods to the data of tropical storms that occurred on the mainland of China since 1949.