Estimating heterogeneous treatment effects from observational data is a crucial task across many fields, helping policy and decision-makers take better actions. There has been recent progress on robust and efficient methods for estimating the conditional average treatment effect (CATE) function, but these methods often do not take into account the risk of hidden confounding, which could arbitrarily and unknowingly bias any causal estimate based on observational data. We propose a meta-learner called the B-Learner, which can efficiently learn sharp bounds on the CATE function under limits on the level of hidden confounding. We derive the B-Learner by adapting recent results for sharp and valid bounds of the average treatment effect (Dorn et al., 2021) into the framework given by Kallus & Oprescu (2023) for robust and model-agnostic learning of conditional distributional treatment effects. The B-Learner can use any function estimator such as random forests and deep neural networks, and we prove its estimates are valid, sharp, efficient, and have a quasi-oracle property with respect to the constituent estimators under more general conditions than existing methods. Semi-synthetic experimental comparisons validate the theoretical findings, and we use real-world data to demonstrate how the method might be used in practice.

翻译：从观测数据中估计异质处理效应是许多领域中的关键任务，有助于政策和决策者采取更优行动。近年来，针对条件平均处理效应（CATE）函数的稳健高效估计方法取得了进展，但这些方法往往未考虑隐藏混淆的风险——隐藏混淆可能任意且未知地偏倚任何基于观测数据的因果估计。我们提出了一种名为B-Learner的元学习器，该学习器能够在隐藏混淆程度受限的条件下高效学习CATE函数的严格界限。通过将针对平均处理效应的严格有效界限（Dorn等人，2021）的最新结果适配至Kallus & Oprescu（2023）提出的用于条件分布处理效应的稳健且模型无关学习框架，我们推导出B-Learner。B-Learner可使用任意函数估计器（如随机森林和深度神经网络），我们证明其估计量具有有效性、严格性、高效性，并在比现有方法更通用的条件下，相对于其组成估计量满足准Oracle性质。半合成实验比较验证了理论发现，并利用真实世界数据展示了该方法在实际中的应用潜力。