提出无需密度比估计的代理因果学习新方法，适用于高维连续处理变量。
Density Ratio-based Proxy Causal Learning Without Density Ratios
- 利用核岭回归构建代理变量因果估计器，避免复杂密度比计算
- 在合成与真实数据集上表现优于或相当现有框架
- 适合处理高维连续干预的因果推断任务
针对存在隐含混杂因素时的代理因果学习（PCL）问题，本文提出一种实用且高效的方法。传统方法中需估计密度比，但在高维场景下困难重重。本文提出的第二类方法绕开了显式密度比估计，适用于连续和高维处理变量。通过核岭回归推导出剂量反应曲线及条件剂量反应曲线的闭式解，并提供一致性保证。实验表明，在合成数据与真实数据集上，该方法性能优于或相当现有框架。
原文摘要 · Abstract (English)
We address the setting of Proxy Causal Learning (PCL), which has the goal of estimating causal effects from observed data in the presence of hidden confounding. Proxy methods accomplish this task using two proxy variables related to the latent confounder: a treatment proxy (related to the treatment) and an outcome proxy (related to the outcome). Two approaches have been proposed to perform causal effect estimation given proxy variables; however only one of these has found mainstream acceptance, since the other was understood to require density ratio estimation - a challenging task in high dimensions. In the present work, we propose a practical and effective implementation of the second approach, which bypasses explicit density ratio estimation and is suitable for continuous and high-dimensional treatments. We employ kernel ridge regression to derive estimators, resulting in simple closed-form solutions for dose-response and conditional dose-response curves, along with consistency guarantees. Our methods empirically demonstrate superior or comparable performance to existing frameworks on synthetic and real-world datasets.
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