摘要
罗宾因果推断模型在非实验数据分析中具有重要地位,但对高维数据分析,古典低维空间处置效应估计量往往表现欠佳.本文结合高维空间下的双重选择估计与群组套索回归,提出一种估计高维稀疏空间下多值处置效应的双重群组套索估计方法.数值模拟发现,对于因果参数估计,双重群组套索估计的经验功效接近理论值,而预测性套索回归则存在较大的功效偏差.对教育生产函数的案例研究发现,该方法可以有效地从多个备选控制变量中选出正确的控制变量,仅有一个噪声变量被错误选择.
The Rubin causal model is a cornerstone in observational data analysis.However,classical treatment effect estimators do not perform well in high dimensional space.This article combines the post-LASSO double selection method and the grouped LASSO method to construct the grouped LASSO double selection estimator (GLDSE)for multi-valued treatment effects in high dimensional sparse space. The numerical simulation confirms that,the predictive LASSO regression shows significant empirical size biases,while the GLDSE has much lower biases.A case study about educational production function shows that,out of many potential control variables,the GLDSE selects five true variables from them.Moreover, only one noise variable is selected.
作者
马键
胡毅
林建浩
MA Jian;HU Yi;LIN Jianhao(School of Economics and Statistics,Guangzhou University,Guangzhou 510006,China;School of Economics and Management,University of Chinese Academy of Sciences,Beijing 100190,China;Lingnan College,Sun Yat-sen University,Guangzhou 510275,China)
出处
《系统工程理论与实践》
EI
CSSCI
CSCD
北大核心
2018年第11期2750-2761,共12页
Systems Engineering-Theory & Practice
基金
国家自然科学基金(71503056)
国家公派高级研究学者、访问学者、博士后项目(201608440100)
国家社会科学基金(18AJL004,16CJL010)~~
