There is a crucial need for practical evidence-based approaches in public policy. This article proposes Bayesian adaptive trials (BATs) as both an efficient method to conduct trials and as a unifying framework for the evaluation of social policy interventions. This approach addresses limitations inherent in traditional methods, such as randomised controlled trials, and recognises the need for evidence-based approaches in public policy.

The proposed approach aims to lower barriers to the adoption of evidence-based methods and to align evaluation processes more closely with the dynamic nature of policy cycles. BATs, grounded in decision theory, offer a dynamic approach which is particularly useful in policy settings, allowing for timely and context-sensitive decisions. BATs’ ability to value potential future information sources position it as an optimal strategy for sequential data acquisition during policy implementation. 

The article acknowledges the assumptions and models intrinsic to BATs, such as prior distributions and likelihood functions, but argues that these are advantageous for decision-makers in social policy and effectively merge the best features of various methodologies.