Causal Random Forests Model Using Instrumental Variable Quantile Regression

被引：12

作者：

Chen, Jau-er ^{[1
,2
]}

Hsiang, Chen-Wei ^{[3
]}

机构：

[1] Tokyo Int Univ, Inst Int Strategy, 1-13-1 Matobakita Kawagoe, Saitama 3501197, Japan

[2] Natl Taiwan Univ, Ctr Res Econometr Theory & Applicat, 1,Sect 4,Roosevelt Rd, Taipei 10617, Taiwan

[3] Natl Taiwan Univ, Behav & Data Sci Res Ctr, 1,Sect 4,Roosevelt Rd, Taipei 10617, Taiwan

来源：

ECONOMETRICS | 2019年 / 7卷 / 04期

关键词：

quantile treatment effect; instrumental variable; quantile regression; causal machine learning; random forests;

D O I：

10.3390/econometrics7040049

中图分类号：

F [经济];

学科分类号：

02 ;

摘要：

We propose an econometric procedure based mainly on the generalized random forests method. Not only does this process estimate the quantile treatment effect nonparametrically, but our procedure yields a measure of variable importance in terms of heterogeneity among control variables. We also apply the proposed procedure to reinvestigate the distributional effect of 401(k) participation on net financial assets, and the quantile earnings effect of participating in a job training program.

引用

页数：22

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