SNPL: Simultaneous Policy Learning and Evaluation for Safe Multi-Objective Policy Improvement
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SNPL：面向安全多目标改进的同步策略学习与评估
- 研究问题
- 如何利用同一份离线数据学习策略，在提高目标结局的同时，以高置信度保证约束性结局不恶化，尤其面对低信噪比与大型策略类？
- 方法与思路
- 作者利用算法稳定性构建 SNPL，使策略学习与评估共享完整数据，并分别提出有限样本和渐近版本的安全保证。
- 材料陈述的贡献
- 摘要称，两种算法可为目标改善或约束性结局不恶化提供高概率保证，并在短信发送个性化应用中报告较大改进。
- 阅读关注点
- 应核对稳定性要求、安全保证的概率对象、策略选择后的推断有效性，以及短信应用中的基准、样本量和提升计算口径。
- 阅读边界
- 这是 arXiv 预印本，不能据此认定已同行评审或获期刊接收；摘要未列保证条件，应用中的提升幅度不能直接推广到其他策略问题。
原文摘要
To design effective digital interventions, experimenters face the challenge of learning decision policies that balance multiple objectives using offline data. Often, they aim to develop policies that maximize goal outcomes, while ensuring there are no undesirable changes in guardrail outcomes. To provide credible recommendations, experimenters must not only identify policies that satisfy the desired changes in goal and guardrail outcomes, but also offer probabilistic guarantees about the changes these policies induce. In practice, however, policy classes are often large, and digital experiments tend to produce datasets with small effect sizes relative to noise. In this setting, standard approaches such as data splitting or multiple testing often result in unstable policy selection and/or insufficient statistical power. In this paper, we provide safe noisy policy learning (SNPL), a novel approach that leverages the concept of algorithmic stability to address these challenges. Our method enables policy learning while simultaneously providing high-confidence guarantees using the entire dataset, avoiding the need for data-splitting. We present finite-sample and asymptotic versions of our algorithm that ensure the recommended policy satisfies high-probability guarantees for avoiding guardrail regressions and/or achieving goal outcome improvements. We test both variants of our approach approach empirically on a real-world application of personalizing SMS delivery. Our results on real-world data suggest that our approach offers dramatic improvements in settings with large policy classes and low signal-to-noise across both finite-sample and asymptotic safety guarantees, offering up to 300\% improvements in detection rates and 150\% improvements in policy gains at significantly smaller sample sizes.