  Many scientific and engineering challenges -- ranging from personalized
medicine to customized marketing recommendations -- require an understanding of
treatment effect heterogeneity. In this paper, we develop a non-parametric
causal forest for estimating heterogeneous treatment effects that extends
Breiman's widely used random forest algorithm. In the potential outcomes
framework with unconfoundedness, we show that causal forests are pointwise
consistent for the true treatment effect, and have an asymptotically Gaussian
and centered sampling distribution. We also discuss a practical method for
constructing asymptotic confidence intervals for the true treatment effect that
are centered at the causal forest estimates. Our theoretical results rely on a
generic Gaussian theory for a large family of random forest algorithms. To our
knowledge, this is the first set of results that allows any type of random
forest, including classification and regression forests, to be used for
provably valid statistical inference. In experiments, we find causal forests to
be substantially more powerful than classical methods based on nearest-neighbor
matching, especially in the presence of irrelevant covariates.
