Causal Random Forests Model Using Instrumental Variable Quantile Regression
Jau-er Chen and
Chen-Wei Hsiang ()
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Chen-Wei Hsiang: Behavioral and Data Science Research Center, National Taiwan University, No. 1, Section 4, Roosevelt Road, Taipei 10617, Taiwan
Econometrics, 2019, vol. 7, issue 4, 1-22
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.
Keywords: quantile treatment effect; instrumental variable; quantile regression; causal machine learning; random forests (search for similar items in EconPapers)
JEL-codes: B23 C C00 C01 C1 C2 C3 C4 C5 C8 (search for similar items in EconPapers)
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Persistent link: https://EconPapers.repec.org/RePEc:gam:jecnmx:v:7:y:2019:i:4:p:49-:d:298392
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