We propose the orthogonal random forest, an algorithm that combines
Neyman-orthogonality to reduce sensitivity with respect to estimation error of
nuisance parameters 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 assumptions 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 $\ell_1$-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 and real data.

我们提出了正交随机森林算法，该算法结合了 Neyman - 正交减少估计误差对无关参数的敏感度，与广义随机森林，通过随机森林对条件矩模型进行灵活的非参数统计估计。我们提供了一致性速率，并为我们的估计器建立了渐进正态性。我们展示了在无关参数的一致性速率具有渐近性的温和假设下，我们可以实现与先验知识相同的错误率。我们展示了当无关函数具有局部稀疏参数化时，本地 l1 - 惩罚回归实现所需速率。我们将我们的方法应用于从离散或连续处理的观察数据中估计异质处理效应，并且我们显示，与先前的工作不同，我们的方法可以在标准稀疏条件下控制高维变量集。我们还对我们算法在合成和实际数据上进行了全面的实证评估。