We develop a nonparametric causal forest for estimating heterogeneous treatment effects that extends Breiman’s widely used random forest algorithm.Expand

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… Expand

There are many settings where researchers are interested in estimating average treatment effects and are willing to rely on the unconfoundedness assumption, which requires that the treatment… Expand

We develop a natural semi-supervised algorithm that uses unlabeled data to create a better adaptive regularizer for generalized linear models, and show that it consistently boosts the performance of dropout training.Expand

We propose a new approach to this problem motivated by the theory of semiparametrically efficient estimation, and establish strong guarantees for the asymptotic utilitarian regret of the resulting policy.Expand

Flexible estimation of heterogeneous treatment effects lies at the heart of many statistical challenges, such as personalized medicine and optimal resource allocation.Expand

Summary
We consider a multiple-hypothesis testing setting where the hypotheses are ordered and one is only permitted to reject an initial contiguous block of hypotheses. A rejection rule in this… Expand

We provide a unified analysis of the predictive risk of ridge regression and regularized discriminant analysis in a dense random effects model. We work in a high-dimensional asymptotic regime where… Expand

We apply causal forests to a dataset derived from the National Study of Learning Mindsets, and consider resulting practical and conceptual challenges. In particular, we discuss how causal forests use… Expand