Transfer Learning of CATE with Kernel Ridge Regression
基于核岭回归的条件平均处理效应迁移学习AI 解读 ↓
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基于核岭回归的条件平均处理效应迁移学习
- 研究问题
- 目标人群没有结果观测时，如何利用来源人群估计条件平均处理效应，并适应跨人群及来源人群处理组间的弱重叠？
- 方法与思路
- 作者将有标签来源样本分成两部分，以回归调整和伪结果训练核岭回归候选模型，再结合留出样本与无标签目标数据选择模型。
- 材料陈述的贡献
- 摘要称给出尖锐的非渐近均方误差界，刻画对两类弱重叠及效应函数复杂度的适应性，并用数值研究和两个真实数据集展示表现。
- 阅读关注点
- 应核对跨人群效应迁移的识别条件、两类重叠如何进入误差界，以及伪结果构造、样本划分和模型选择的具体实现。
- 阅读边界
- 仅据所给OpenAlex摘要版本，不能推断后续刊本变化。摘要未说明迁移识别假设和数据集细节，所述效率优势不能视为普遍结论。
原文摘要
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 the CATE function. Extensive numerical studies confirm that our method achieves superior finite-sample efficiency and adaptability. We conclude by demonstrating the effectiveness and superiority of our approach on two real-world datasets.