摘要
基于Neyman-Rubin潜在结果框架,构建k近邻核估计量来测度响应变量随机缺失情形下的条件平均处理效应(conditional average treatment effect,CATE),旨在评估不同处理方式对个体的影响.证明了k近邻核估计量的几乎完全收敛性和渐近正态性.数值模拟表明k近邻核估计量的表现优良,利用真实数据进行实证分析,实证结果显示k近邻核估计量具有较小的平均绝对偏差和均方根误差.
Under the Neyman-Rubin potential outcome framework,we construct a k-nearest neighbor kernel estimator to measure the conditional average treatment effect in the case of random missing response variables,aiming to evaluate the impact of different treatments on individuals.The paper proves the almost complete convergence and the asymptotic normality of the estimator.The numerical simulation shows that the k-nearest neighbor kernel estimator performs well.The real-world data is used for empirical analysis,and the empirical results show that mean absolute error and root mean square error of the k-nearest neighbor kernel estimator are smaller.
作者
曾华俊
明瑞星
苏培娟
黄绍航
肖敏
Huajun Zeng;Ruixing Ming;Peijuan Su;Shaohang Huang;Min Xiao(Collaborative Innovation Center of Statistical Data Engineering Technology&Application,Zhejiang Gongshang University,Hangzhou 310018;School of Statistics and Mathematics,Zhejiang Gongshang University,Hangzhou 310018;Economic Forecasting and Policy Simulation Laboratory,Zhejiang Gongshang University,Hangzhou 310018)
出处
《数学物理学报(A辑)》
北大核心
2025年第3期992-1012,共21页
Acta Mathematica Scientia
基金
浙江省社会科学规划课题(22GXSZ001Z)
数字+学科建设项目(SZJ2022B004)
浙江省教育厅一般项目(Y202353084)
浙江省登峰学科(浙江工商大学统计学)项目
浙江省省属高校基本科研业务费专项资金(XT202302)。
关键词
条件平均处理效应
随机缺失
k近邻核估计量
渐近正态性
conditional average treatment effect
random missing
k-nearest neighbor kernel estimator
asymptotic normality
