提出自适应正则化方法，提升低重叠场景下的治疗效果估计精度。
Overlap-Adaptive Regularization for Conditional Average Treatment Effect Estimation
- 根据重叠权重动态调整正则化强度，低重叠区域正则更强。
- 在低重叠数据上显著优于固定正则化方法，误差更小。
- 兼容各类元学习器，适合个性化医疗中的因果推断任务。
条件平均处理效应（CATE）广泛用于个性化医疗以指导治疗决策。然而，现有先进的CATE估计方法（即元学习器）在重叠度较低时表现不佳。本文提出一种新方法——重叠自适应正则化（OAR），通过按重叠权重比例调节目标模型的正则化强度，使低重叠区域的正则化更强。据我们所知，OAR是首个将重叠权重引入元学习器正则项的方法。该方法灵活，可适配任意现有CATE元学习器，支持参数与非参数第二阶段模型。此外，我们提出了去偏版本的OAR，保持了原有元学习器的奈曼正交性，确保推断更稳健。通过一系列（半）合成实验验证，OAR在低重叠设置下显著优于常数正则化方法。
原文摘要 · Abstract (English)
The conditional average treatment effect (CATE) is widely used in personalized medicine to inform therapeutic decisions. However, state-of-the-art methods for CATE estimation (so-called meta-learners) often perform poorly in the presence of low overlap. In this work, we introduce a new approach to tackle this issue and improve the performance of existing meta-learners in the low-overlap regions. Specifically, we introduce Overlap-Adaptive Regularization (OAR) that regularizes target models proportionally to overlap weights so that, informally, the regularization is higher in regions with low overlap. To the best of our knowledge, our OAR is the first approach to leverage overlap weights in the regularization terms of the meta-learners. Our OAR approach is flexible and works with any existing CATE meta-learner: we demonstrate how OAR can be applied to both parametric and non-parametric second-stage models. Furthermore, we propose debiased versions of our OAR that preserve the Neyman-orthogonality of existing meta-learners and thus ensure more robust inference. Through a series of (semi-)synthetic experiments, we demonstrate that our OAR significantly improves CATE estimation in low-overlap settings in comparison to constant regularization.
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