  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.
