방법 비교

	Policy Feedback Analysis	Realist Evaluation
분야	Public Administration	Public Policy
계열	Process / pipeline	Process / pipeline
기원 연도	1993	1997
창시자	Paul Pierson	Ray Pawson & Nick Tilley
유형	Theoretical-analytical framework for policy effects on politics	Theory-driven, generative evaluation approach
원전	Pierson, P. (1993). When Effect Becomes Cause: Policy Feedback and Political Change. World Politics, 45(4), 595–628.	Pawson, R., & Tilley, N. (1997). Realistic Evaluation. London: SAGE Publications. ISBN: 9780761950097
별칭	Policy Feedback Theory Analysis, Feedback Effects Analysis, Policy-as-Cause Analysis, Self-Reinforcing Policy Analysis	Realistic Evaluation, Theory-Driven Realist Evaluation, CMO Configuration Analysis, Pawson-Tilley Evaluation
관련	4	4
요약	Policy feedback analysis examines how policies, once enacted, reshape the politics that follow — turning yesterday's policy effects into today's political causes. Drawing on Paul Pierson's foundational 1993 article 'When Effect Becomes Cause,' it holds that policies are not just outputs of politics but powerful forces that create resources and incentives for groups, build administrative capacities, and shape how citizens understand their interests and their government. By tracing these resource and interpretive feedback effects over time, the method explains why some policies become self-reinforcing and politically durable, why others undermine their own support, and why policy change is often path-dependent and hard to reverse.	Realist evaluation is a theory-driven approach to evaluating programs and policies that asks not simply 'does it work?' but 'what works, for whom, in what circumstances, and why?'. Developed by Ray Pawson and Nick Tilley in their 1997 book Realistic Evaluation, it treats interventions as theories incarnate: programs offer resources or opportunities that trigger underlying mechanisms of reasoning and response in participants, and those mechanisms only fire in particular contexts. The unit of analysis is the Context-Mechanism-Outcome (CMO) configuration, and the goal is to build and refine middle-range theory that explains differential outcomes across settings.