  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.
