Before revision:  We propose orthogonal random forest, an algorithm that combines orthogonalization, a technique that effectively removes the confounding effect in two-stage estimation, with generalized random forests  Athey et al., 2017 , a flexible method for estimating treatment effect heterogeneity.
After revision:  We propose orthogonal random forest, an algorithm that incorporates double machine learning--a method of using Neyman-orthogonal moments to reduce sensitivity with respect to nuisance parameters to estimate the target parameter--with generalized random forests Athey et al., 2017 , a flexible method for estimating treatment effect heterogeneity.