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title: "Safe Bayesian Optimization with Counterfactual Policies — Stuff That Spins"
description: "arXiv:2607.05620v1 Announce Type: new Abstract: In many decision-making settings, new interventions are acceptable only if they do not reduce outcomes below so…"
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date: "2026-07-08T04:00:00+00:00"
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# Safe Bayesian Optimization with Counterfactual Policies

**Source:** Unknown  
**Published:** July 8, 2026  
**Original:** https://arxiv.org/abs/2607.05620  

## On this page

- [Overview](#overview)

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## Overview

arXiv:2607.05620v1 Announce Type: new Abstract: 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 baseli

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