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.

翻译：工具变量（IV）与近端因果学习（Proxy）方法是处理未观测混杂因素下因果推断的核心框架。尽管方法学已取得重大进展，现有方法却很少能提供可靠的认识不确定性（EU）量化。我们通过解条件高斯过程（DGP）框架来解决这一缺口，实现具有不确定性感知的因果学习。我们的公式将流行的核估计量恢复为后验均值，从而确保预测精度，而后验方差则产生原则性且校准良好的EU。此外，概率结构支持通过边缘对数似然优化进行系统化模型选择。实证结果表明，该方法在通过经验覆盖频率和决策感知的准确率-拒绝曲线评估时，展现出强大的预测性能与信息丰富的EU量化能力。综上，我们的方法为未观测混杂下的因果推断提供了一个统一且实用的解决方案，并具备可靠的不确定性度量。