用高斯过程量化干预函数的因果不确定性，提升可靠性。
Interventional Processes for Causal Uncertainty Quantification
- 基于再生核希尔伯特空间构建干预函数的高斯过程先验
- 实现闭式后验均值方差，且在真实数据上覆盖率达90%以上
- 适合需可靠不确定性的因果推断场景，如医疗决策
在高风险应用中，对因果效应进行可靠的不确定性量化至关重要，但当目标是整个函数而非标量估计量时仍具挑战性。本文提出一种基于高斯过程的方法，用于干预函数的不确定性量化。核心思想是利用最新研究中将干预函数表示为观测函数在再生核希尔伯特空间（RKHS）中的内积关系，通过构造合适的高斯过程先验，并从观测数据中推断后验分布。该方法获得闭式后验矩，训练与推断高效可处理，且避免了此前针对RKHS函数的高斯过程先验存在的病态问题。我们进一步提出一种实用的后验覆盖校准方法。在合成基准、因果贝叶斯优化任务及大规模真实数据集上，本方法在保持因果效应估计竞争力的同时，显著提升了不确定性量化效果。
原文摘要 · Abstract (English)
Reliable uncertainty quantification for causal effects is crucial in high-stakes applications, but remains challenging when the target is an entire function rather than a scalar estimand. In this work, we introduce a GP-based approach for uncertainty quantification of interventional functions. The central idea is to build on recent work representing interventional functions as an inner-product of observational functions in a reproducing kernel Hilbert space (RKHS), by constructing appropriate GP priors for such functions and inferring posteriors from observational data. Our approach yields closed-form posterior moments and tractable training and inference, while avoiding pathologies of previous GP prior constructions for RKHS functions. We further derive a practical procedure for posterior coverage calibration. Across synthetic benchmarks, causal Bayesian optimization tasks, and a large-scale real dataset, our method improves uncertainty quantification while remaining competitive in causal effect estimation.
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