Random forests and selected samples

Jonathan A. Cook and Saad Siddiqui

Bulletin of Economic Research, 2020, vol. 72, issue 3, 272-287

Abstract: This paper presents a procedure for recovering causal coefficients from selected samples that uses random forests, a popular machine‐learning algorithm. This proposed method makes few assumptions regarding the selection equation and the distribution of the error terms. Our Monte Carlo results indicate that our method performs well, even when the selection and outcome equations contain the same variables, as long as the selection equation is nonlinear. The method can also be used when there are many variables in the selection equation. We also compare the results of our procedure with other parametric and semiparametric methods using real data.

Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
https://doi.org/10.1111/boer.12222

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:bla:buecrs:v:72:y:2020:i:3:p:272-287

Ordering information: This journal article can be ordered from
http://www.blackwell ... bs.asp?ref=0307-3378

Access Statistics for this article

More articles in Bulletin of Economic Research from Wiley Blackwell
Bibliographic data for series maintained by Wiley Content Delivery ().