Causal Inference and Policy Evaluation from Case Studies Using Bayesian Process Tracing

Abstract

AbstractCase studies enable policy-relevant causal inferences when experimental and quasi-experimental methods are not possible. Even when other methods are possible, case studies can strengthen inferences either as a standalone method or as part of a multimethod research design. The chapter outlines the case study method of process tracing (PT), which is a within-case mode of analysis that builds upon Bayesian logic to make inferences to the best explanation of the outcomes of single cases. The chapter locates the epistemological basis of PT in the development and testing of theories about the ways in which causal mechanisms operate to generate outcomes. It then defines PT and outlines best practices on how to do it, illustrating these with examples of case study research on the COVID pandemic. The chapter then outlines the comparative advantages of PT vis-à-vis other methods, and identifies the kinds of research questions and research contexts for which PT is most useful. This leads to a brief discussion of two methodological innovations: formal Bayesian PT and the use of causal models in the form of Directed Acyclic Graphs to assist PT and integrate qualitative and quantitative evidence. The chapter concludes with the strengths and limits of PT.