The study of causal mechanisms interests scholars across the social sciences. Case studies can be a valuable tool in developing knowledge and hypotheses about how causal mechanisms function. The usefulness of case studies in the search for causal mechanisms depends on effective case selection, and there are few existing guidelines for selecting cases to study causal mechanisms. We outline a general approach for selecting cases for pathway analysis: a mode of qualitative research that is part of a mixed-method research agenda, which seeks to (1) understand the mechanisms or links underlying an association between some explanatory variable, X1, and an outcome, Y, in particular cases and (2) generate insights from these cases about mechanisms in the unstudied population of cases featuring the X1/Y relationship. The gist of our approach is that researchers should choose cases for comparison in light of two criteria. The first criterion is the expected relationship between X1/Y, which is the degree to which cases are expected to feature the relationship of interest between X1 and Y. The second criterion is variation in case characteristics or the extent to which the cases are likely to feature differences in characteristics that can facilitate hypothesis generation. We demonstrate how to apply our approach and compare it to a leading example of pathway analysis in the so-called resource curse literature, a prominent example of a correlation featuring a nonlinear relationship and multiple causal mechanisms.