Before revision: We study the problem of estimating heterogeneous treatment effects from observational data, where the treatment policy on the collected data was determined by potentially many confounding observable variables. 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 the 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.