The proliferation of data has sparked significant interest in leveraging findings from one study to estimate treatment effects in a different target population without direct outcome observations. However, the transfer learning process is frequently hindered by substantial covariate shift and limited overlap between (i) the source and target populations, as well as (ii) the treatment and control groups within the source. We propose a novel method for overlap-adaptive transfer learning of conditional average treatment effect (CATE) using kernel ridge regression (KRR). Our approach involves partitioning the labeled source data into two subsets. The first one is used to train candidate CATE models based on regression adjustment and pseudo-outcomes. An optimal model is then selected using the second subset and unlabeled target data, employing another pseudo-outcome-based strategy. We provide a theoretical justification for our method through sharp non-asymptotic MSE bounds, highlighting its adaptivity to both weak overlaps and the complexity of CATE function. Extensive numerical studies confirm that our method achieves superior finite-sample efficiency and adaptability. We conclude by demonstrating the effectiveness of our approach using a 401(k) eligibility dataset.

翻译：数据激增引发了学界浓厚兴趣，即如何利用一项研究的结果来估计不同目标人群的处理效应，而无需直接观测其结局。然而，迁移学习过程常受到以下两方面严重阻碍：(i) 源人群与目标人群间存在显著的协变量偏移与有限重叠，以及(ii) 源数据中处理组与对照组之间重叠不足。本文提出一种基于核岭回归（KRR）的重叠自适应条件平均处理效应（CATE）迁移学习方法。该方法首先将带标签的源数据划分为两个子集：第一个子集用于基于回归调整与伪结局训练候选CATE模型；随后利用第二个子集与无标签目标数据，采用另一种基于伪结局的策略选择最优模型。我们通过严格的非渐近均方误差界为方法提供了理论依据，凸显了其对弱重叠性与CATE函数复杂度的自适应能力。大量数值研究表明，该方法在有限样本下实现了优异的效率与适应性。最后，我们通过401(k)资格数据集验证了所提方法的有效性。