Automatic Doubly Robust Forests

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.