Bayesian Adaptive Trials for Social Policy

TL;DR

This paper introduces Bayesian Adaptive Trials (BAT) as an innovative, flexible framework for evaluating social policies, aiming to improve timeliness, efficiency, and decision-making in policy assessment compared to traditional RCTs.

Contribution

It presents BAT as a novel adaptive evaluation method that integrates diverse information and supports dynamic, iterative policy assessment in social settings.

Findings

BAT enables more timely policy decisions.

BAT incorporates diverse data sources effectively.

BAT offers a unified framework for policy evaluation.

Abstract

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.