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 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.