Generalized Random Forests

Susan Athey, Julie Tibshirani and Stefan Wager
Additional contact information
Julie Tibshirani: Stanford University
Stefan Wager: Stanford University

Research Papers from Stanford University, Graduate School of Business

Abstract: We propose generalized random forests, a method for non-parametric statistical estimation based on random forests (Breiman, 2001) that can be used to fit any quantity of interest identified as the solution to a set of local moment equations. Following the literature on local maximum likelihood estimation, our method operates at a particular point in covariate space by considering a weighted set of nearby training examples; however, instead of using classical kernel weighting functions that are prone to a strong curse of dimensionality, we use an adaptive weighting function derived from a forest designed to express heterogeneity in the specified quantity of interest. We propose a flexible, computationally efficient algorithm for growing generalized random forests, develop a large sample theory for our method showing that our estimates are consistent and asymptotically Gaussian, and provide an estimator for their asymptotic variance that enables valid confidence intervals. We use our approach to develop new methods for three statistical tasks: non-parametric quantile regression, conditional average partial effect estimation, and heterogeneous treatment effect estimation via instrumental variables. A software implementation, grf for R and C++, is available from CRAN.

Date: 2017-07
New Economics Papers: this item is included in nep-big and nep-cmp
References: Add references at CitEc
Citations: View citations in EconPapers (11)

Downloads: (external link)
https://www.gsb.stanford.edu/gsb-cmis/gsb-cmis-download-auth/441181
Our link check indicates that this URL is bad, the error code is: 404 Not Found

Related works:
Working Paper: Generalized Random Forests (2018)
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:ecl:stabus:3575

Access Statistics for this paper

More papers in Research Papers from Stanford University, Graduate School of Business Contact information at EDIRC.
Bibliographic data for series maintained by ().