Abstract

This article explores how policy learning can improve comparative policy analysis by focusing on causality in learning processes. After summarizing the comparative credentials of the policy learning literature, the article outlines a framework of four learning modes, relating it to three approaches of causality: deterministic, probabilistic, and set-theoretic. It then builds 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. The article concludes by showing how these challenges are addressed and suggesting implications for further research.

摘要

本文探讨了政策学习如何通过关注学习过程中的因果关系来改进比较政策分析。在总结了政策学习文献的比较凭据后，本文概述了四种学习模式的框架，并将其与三种因果关系方法相关联：确定性、概率性和集合论。然后在此基础上探索与以下方面相关的因果关系和学习的不同方法：政策变化、政治背景，最后是比较政策分析的时间和空间维度。文章最后展示了如何应对这些挑战，并提出了对进一步研究的启示。