Generalized Random Forests

TL;DR

This paper introduces generalized random forests, a flexible non-parametric estimation method that adapts to heterogeneity in data, providing consistent, asymptotically normal estimates with confidence intervals for various statistical tasks.

Contribution

It develops a novel adaptive weighting scheme for random forests, extending their application to a broad class of local moment problems with theoretical guarantees.

Findings

Method is consistent and asymptotically Gaussian.

Enables valid confidence intervals for complex estimands.

Successfully applied to quantile regression and treatment effect estimation.

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 considers 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.