This paper proposes the automatic Doubly Robust Random Forest (DRRF) algorithm for estimating the conditional expectation of a moment functional in the presence of high-dimensional nuisance functions. DRRF combines the automatic debiasing framework using the Riesz representer (Chernozhukov et al., 2022) with non-parametric, forest-based estimation methods for the conditional moment (Athey et al., 2019; Oprescu et al., 2019). In contrast to existing methods, DRRF does not require prior knowledge of the form of the debiasing term nor impose restrictive parametric or semi-parametric assumptions on the target quantity. Additionally, it is computationally efficient for making predictions at multiple query points and significantly reduces runtime compared to methods such as Orthogonal Random Forest (Oprescu et al., 2019). We establish the consistency and asymptotic normality results of DRRF estimator under general assumptions, allowing for the construction of valid confidence intervals. Through extensive simulations in heterogeneous treatment effect (HTE) estimation, we demonstrate the superior performance of DRRF over benchmark approaches in terms of estimation accuracy, robustness, and computational efficiency.

翻译：本文提出了一种自动双重稳健随机森林（DRRF）算法，用于在高维干扰函数存在的情况下估计矩泛函的条件期望。DRRF将使用Riesz表示子的自动去偏框架（Chernozhukov等人，2022）与非参数、基于森林的条件矩估计方法（Athey等人，2019；Oprescu等人，2019）相结合。与现有方法相比，DRRF既不需要预先了解去偏项的具体形式，也不对目标量施加限制性的参数或半参数假设。此外，该算法在多个查询点进行预测时计算效率高，与正交随机森林（Oprescu等人，2019）等方法相比显著减少了运行时间。我们在一般性假设下建立了DRRF估计量的一致性和渐近正态性结果，从而能够构建有效的置信区间。通过在异质性处理效应（HTE）估计中进行大量模拟，我们证明了DRRF在估计精度、鲁棒性和计算效率方面均优于基准方法。