Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/247549 
Year of Publication: 
2019
Citation: 
[Journal:] Econometrics [ISSN:] 2225-1146 [Volume:] 7 [Issue:] 4 [Publisher:] MDPI [Place:] Basel [Year:] 2019 [Pages:] 1-22
Publisher: 
MDPI, Basel
Abstract: 
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.
Subjects: 
causal machine learning
instrumental variable
quantile regression
quantile treatment effect
random forests
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by Logo
Document Type: 
Article

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