用反事实预测保障优化安全，避免干预后效果低于基准线。
Safe Bayesian Optimization with Counterfactual Policies
- 基于置信区间估计基线政策的反事实结果，确保安全约束可靠。
- 在真实医疗场景中验证，可控制约束违规率不超过设定阈值。
- 适用于存在协变量偏移的临床决策等高风险领域。
在许多决策场景中，新干预措施仅在不使结果低于某基准阈值时才可接受。例如，在临床医学中，新疗法必须不劣于现有标准治疗。安全贝叶斯优化在满足安全约束的前提下最大化目标函数。本文研究的场景中，安全是相对于一个已知基线策略定义的，其结果为反事实且无法直接观测。因此需估计基线策略的反事实结果，并利用这些不确定的估计来安全地优化目标。我们通过使用分位数回归和可调置信区间方法构建有效的反事实基线结果不确定性区间，并将其整合进安全贝叶斯优化框架，以保证约束违反率不超过用户指定水平。此外，我们还展示了如何适应不同类型的协变量偏移。论文提供了安全性证明、实验验证及敏感性分析。
原文摘要 · Abstract (English)
In many decision-making settings, new interventions are acceptable only if they do not reduce outcomes below some established threshold. For example, in clinical medicine, new treatments are often acceptable only if they do not worsen outcomes relative to an established standard of care. Safe Bayesian optimization maximizes an objective subject to safety constraints. In the setting that we consider here, safety is defined relative to a known baseline policy whose outcomes are counterfactual and therefore unobserved. Thus, the counterfactual outcomes of the baseline policy must be estimated and those (uncertain) estimates must be used to safely optimize the objective. We address this estimation problem by using conformal prediction to construct valid uncertainty intervals for counterfactual baseline outcomes, and we show how these intervals can be integrated into safe Bayesian optimization to ensure that constraint violations occur at or below a user-specified rate. We also show how to adapt these conformal estimates to different kinds of covariate shift. We provide a safety proof, experimental evidence, and a sensitivity analysis.
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