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. We prove a consistency rate result of our estimatorin the partially linear regression model, and en route we provide a consistency analysis for a general framework of performing generalized method of moments (GMM) estimation . We also provide a comprehensive empirical evaluation of our algorithms , and show that they consistently outperform baseline approaches.
We propose the 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 non-parametric method for statistical estimation of conditional moment models using random forests. We provide a consistency rate and establish asymptotic normality for our estimator. We show that under mild assumption on the consistency rate of the nuisance estimator, we can achieve the same error rate as an oracle with a priori knowledge of these nuisance parameters. We show that when the nuisance functions have a locally sparse parametrization, then a local l1-penalized regression achieves the required rate. We apply our method to estimate heterogeneous treatment effects from observational data with discrete treatments or continuous treatments, and we show that, unlike prior work, our method provably allows to control for a high-dimensional set of variables under standard sparsity conditions . We also provide a comprehensive empirical evaluation of our algorithm on both synthetic data and real data , and show that it consistently outperforms baseline approaches.
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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
propose the
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combines orthogonalization, a technique that effectively removes the confounding effect in two-stage estimation, with
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
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, a flexible method for estimating treatment effect heterogeneity. We prove
)--a flexible non-parametric method for statistical estimation of conditional moment models using random forests. We provide
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result of our estimatorin the partially linear regression model, and en route we provide a consistency analysis for a general framework of performing generalized method of moments (GMM) estimation
and establish asymptotic normality for our estimator. We show that under mild assumption on the consistency rate of the nuisance estimator, we can achieve the same error rate as an oracle with a priori knowledge of these nuisance parameters. We show that when the nuisance functions have a locally sparse parametrization, then a local l1-penalized regression achieves the required rate. We apply our method to estimate heterogeneous treatment effects from observational data with discrete treatments or continuous treatments, and we show that, unlike prior work, our method provably allows to control for a high-dimensional set of variables under standard sparsity conditions
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algorithms
algorithm on both synthetic data and real data
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they consistently outperform
it consistently outperforms