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：数据的激增引发了人们对于利用一项研究的结果来估计另一目标人群的处理效应而不需要直接观测结果的极大兴趣。然而，迁移学习过程常常受到以下两方面显著协变量偏移和有限重叠的阻碍：(i) 源人群与目标人群之间，以及 (ii) 源数据内部处理组与对照组之间。我们提出了一种新颖的、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。我们的方法包括将已标记的源数据划分为两个子集。第一个子集用于基于回归调整和伪结果训练候选CATE模型。然后，利用第二个子集和未标记的目标数据，采用另一种基于伪结果的策略来选择最优模型。我们通过尖锐的非渐近均方误差界为我们的方法提供了理论依据，突出了其对弱重叠和CATE函数复杂性的自适应性。大量的数值研究证实，我们的方法在有限样本效率和适应性方面均表现出优越性。最后，我们通过一个401(k)资格数据集展示了我们方法的有效性。