Policy learning in comparative policy analysis

Abstract

We explore how policy learning can improve comparative policy analysis by focussing on causality in learning processes. After summarising the comparative credentials of the policy learning literature, we outline a framework of four learning modes relating it to three approaches of causality: deterministic, probabilistic and set-theoretic. We then build on this to explore different approaches to causation and learning in relation to: policy change, political contexts, and, finally, the temporal and spatial dimensions of comparative policy analysis. We conclude showing how these challenges are addressed and suggest implications for further research.