Causal random forests model using instrumental variable quantile regression

Jau-er Chen and Chen-Wei Hsiang

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.

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Year of publication:	2019
Authors:	Chen, Jau-er ; Hsiang, Chen-Wei
Published in:	Econometrics : open access journal. - Basel : MDPI, ISSN 2225-1146, ZDB-ID 2717594-7. - Vol. 7.2019, 4/49, p. 1-22
Subject:	causal machine learning \| instrumental variable \| quantile regression \| quantile treatment effect \| random forests \| Regressionsanalyse \| Regression analysis \| Kausalanalyse \| Causality analysis \| IV-Schätzung \| Instrumental variables \| Theorie \| Theory \| Künstliche Intelligenz \| Artificial intelligence