This paper proposes Bayesian Adaptive Trials (BAT) as both an efficient method to conduct trials and a unifying framework for evaluation social policy interventions, addressing limitations inherent in traditional methods such as Randomized Controlled Trials (RCT). Recognizing the crucial need for evidence-based approaches in public policy, the proposal aims to lower barriers to the adoption of evidence-based methods and align evaluation processes more closely with the dynamic nature of policy cycles. BATs, grounded in decision theory, offer a dynamic, ``learning as we go'' approach, enabling the integration of diverse information types and facilitating a continuous, iterative process of policy evaluation. BATs' adaptive nature is particularly advantageous in policy settings, allowing for more timely and context-sensitive decisions. Moreover, BATs' ability to value potential future information sources positions it as an optimal strategy for sequential data acquisition during policy implementation. While acknowledging the assumptions and models intrinsic to BATs, such as prior distributions and likelihood functions, the paper argues that these are advantageous for decision-makers in social policy, effectively merging the best features of various methodologies.

翻译：本文提出将贝叶斯自适应试验（BAT）作为开展试验的有效方法及评估社会政策干预的统一框架，以解决随机对照试验（RCT）等传统方法固有的局限性。鉴于公共政策中基于证据方法的迫切需求，该提议旨在降低证据驱动方法的采纳门槛，并使评估流程更贴近政策周期的动态特征。基于决策理论的BAT采用动态的“边学边做”策略，能够整合多元信息类型，推动政策评估的持续迭代进程。BAT的自适应特性在政策场景中尤为适用，可实现更及时且具情境敏感性的决策。此外，BAT评估潜在未来信息来源的能力使其成为政策实施过程中序贯数据采集的最优策略。尽管本文承认BAT所依赖的假设与模型（如先验分布与似然函数），但论证这些要素对社会政策决策者具有独特优势，能有效融合多元方法论的最佳特征。