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Causal Random Forests Model Using Instrumental Variable Quantile Regression

by Jau-er Chen 1,2,* and Chen-Wei Hsiang 3
1
Institute for International Strategy, Tokyo International University, 1-13-1 Matobakita Kawagoe, Saitama 350-1197, Japan
2
Center for Research in Econometric Theory and Applications, National Taiwan University, No. 1, Section 4, Roosevelt Road, Taipei 10617, Taiwan
3
Behavioral and Data Science Research Center, National Taiwan University, No. 1, Section 4, Roosevelt Road, Taipei 10617, Taiwan
*
Author to whom correspondence should be addressed.
Econometrics 2019, 7(4), 49; https://doi.org/10.3390/econometrics7040049
Received: 24 September 2019 / Revised: 8 December 2019 / Accepted: 11 December 2019 / Published: 16 December 2019
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. View Full-Text
Keywords: quantile treatment effect; instrumental variable; quantile regression; causal machine learning; random forests quantile treatment effect; instrumental variable; quantile regression; causal machine learning; random forests
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Chen, J.-E.; Hsiang, C.-W. Causal Random Forests Model Using Instrumental Variable Quantile Regression. Econometrics 2019, 7, 49.

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