提出自动双重稳健随机森林，高效估计高维干扰项下的条件期望。
Automatic Doubly Robust Forests
- 基于里斯表示器自动去偏，无需预设去偏形式。
- 在异质处理效应估计中精度、鲁棒性与效率均优于基准方法。
- 适合高维数据下无需强假设的因果推断任务。
本文提出自动双重稳健随机森林（DRRF）算法，用于在存在高维干扰函数时估计矩函数的条件期望。DRRF将基于里斯表示器的自动去偏框架拓展至条件设定，实现非参数、基于森林的估计（Athey et al., 2019; Oprescu et al., 2019）。与现有方法不同，DRRF无需事先知道去偏项的形式，也无需对目标量施加限制性的参数或半参数假设。此外，其在多个查询点预测上计算高效。在一般假设下，我们建立了DRRF估计量的一致性和渐近正态性，支持有效置信区间的构建。通过在异质处理效应（HTE）估计中的大量模拟实验，证明了DRRF在估计精度、鲁棒性和计算效率方面均显著优于基准方法。
原文摘要 · Abstract (English)
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 extends the automatic debiasing framework based on the Riesz representer to the conditional setting and enables nonparametric, forest-based estimation (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 or impose restrictive parametric or semi-parametric assumptions on the target quantity. Additionally, it is computationally efficient in making predictions at multiple query points. We establish consistency and asymptotic normality results for the 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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。