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 (2022) for robust and model-agnostic learning of 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 demonstrate how the method might be used in practice.

翻译：从观测数据估计异质性处理效应是众多领域中的关键任务，有助于政策制定者和决策者采取更优行动。近期在稳健且高效的条件平均处理效应（CATE）函数估计方法上取得了进展，但这些方法通常未考虑隐藏混杂的风险——这可能导致基于观测数据的任何因果估计产生任意且未知的偏倚。我们提出一种名为B-Learner的元学习器，能够在隐藏混杂水平受限的条件下高效学习CATE函数的尖锐界限。通过将平均处理效应尖锐有效界限的最新成果（Dorn等人，2021）适配至Kallus与Oprescu（2022）提出的稳健且模型无关的分布性处理效应学习框架，我们推导出B-Learner。该学习器可灵活采用随机森林、深度神经网络等任意函数估计器，理论证明其估计量在比现有方法更广泛的条件下具有有效性、尖锐性、高效性，并具备关于组成估计器的拟最优性质。半合成实验比较验证了理论发现，并通过真实数据演示该方法在实际中的应用潜力。