用高斯过程实现因果推断的可信不确定性量化。
Instrumental and Proximal Causal Inference with Gaussian Processes
- 基于条件高斯过程构建因果推断框架，统一处理工具变量与近似变量方法。
- 后验方差提供可校准的不确定性估计，实测覆盖率与拒答曲线表现优异。
- 适合需要可靠置信度的医疗、金融等高风险决策场景使用。
工具变量（IV）和近似因果学习（Proxy）是应对未观测混杂因素时的核心因果推断框架。尽管方法不断进步，现有技术普遍缺乏可靠的信念不确定性（EU）量化能力。本文提出一种去条件高斯过程（DGP）框架，实现面向因果推断的不确定性感知建模。该框架将常见核估计算法作为后验均值恢复，保证预测精度；同时后验方差提供合理且可校准的不确定性估计。其概率结构支持通过边缘对数似然优化进行系统性模型选择。实验表明，该方法在预测性能与不确定性量化方面均表现良好，通过经验覆盖频率与决策感知准确率拒答曲线评估验证。整体上，本方法为未观测混杂下的因果推断提供了统一、实用且具备可靠不确定性的解决方案。
原文摘要 · Abstract (English)
Instrumental variable (IV) and proximal causal learning (Proxy) methods are central frameworks for causal inference in the presence of unobserved confounding. Despite substantial methodological advances, existing approaches rarely provide reliable epistemic uncertainty (EU) quantification. We address this gap through a Deconditional Gaussian Process (DGP) framework for uncertainty-aware causal learning. Our formulation recovers popular kernel estimators as the posterior mean, ensuring predictive precision, while the posterior variance yields principled and well-calibrated EU. Moreover, the probabilistic structure enables systematic model selection via marginal log-likelihood optimization. Empirical results demonstrate strong predictive performance alongside informative EU quantification, evaluated via empirical coverage frequencies and decision-aware accuracy rejection curves. Together, our approach provides a unified, practical solution for causal inference under unobserved confounding with reliable uncertainty.
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