The study of Health in All Policies (HiAP) is gaining momentum. Authors are increasingly turning to wide swathes of political and social theory to frame (Program) Theory Based (or Informed) Evaluation (TBE) approaches. TBE for HiAP is not only prudent, it adds a level of elegance and insight to the research toolbox. However, it is still necessary to organize theoretical thinking appropriately. ...

Privacy policies are notices posted by providers and intended to inform users about privacy practices. However, extant research shows that privacy policies are often of poor quality and do not address users’ concerns. In this paper, we design and develop PPC – a privacy policy content assessment instrument to support assessments of whether offered privacy policy content provides comprehensive i...

1. Cardiovascular Surgery Specialist, Master in Public Policy Evaluation-UFC, Chief of Pediatric Cardiovascular Surgery, Hospital Dr. Carlos Alberto Studart Gomes/SESA, Incor Criança, Fortaleza, CE, Brazil. 2. PhD, Professor of the Master in Public Policy Evaluation Federal University of Ceará, Fortaleza, CE, Brazil. 3. Professor of the Master in Public Policy Evaluation Federal University of C...

We consider the classical policy iteration method of dynamic programming (DP), where approximations and simulation are used to deal with the curse of dimensionality. We survey a number of issues: convergence and rate of convergence of approximate policy evaluation methods, singularity and susceptibility to simulation noise of policy evaluation, exploration issues, constrained and enhanced polic...

Abstract Off-policy evaluation is the problem of evaluating a decision-making policy using data collected under a different behaviour policy. While several methods are available for addressing off-policy evaluation, little work has been done on identifying the best methods. In this paper, we conduct an in-depth comparative study of several off-policy evaluation methods in non-bandit, finite-hor...

We devise algorithms for the policy evaluation problem in reinforcement learning, assuming access to a simulator and certain side information called supergraph. Our explore backward from high-cost states find high-value ones, contrast approaches that work forward all states. While several papers have demonstrated utility of exploration empirically, we conduct rigorous analyses which show our ca...