Safe Bayesian Optimization with Counterfactual Policies
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 estim...
| Authors | Katherine Avery, Bruno Castro da Silva, David Jensen |
|---|---|
| Primary category | cs.LG |
| Published | 6 July 2026 |
| Last updated at source | 9 August 2026 |
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