Selecting Appropriate Cases When Tracing Causal Mechanisms

Abstract

The last decade has witnessed resurgence in the interest in studying the causal mechanisms linking causes and effects. This article games through the methodological consequences that adopting a systems understanding of mechanisms has for what types of cases we should select when using in-depth case study methods like process tracing. The article proceeds in three steps. We first expose the assumptions that underpin the study of causal mechanisms as systems that have methodological implications for case selection. In particular, we take as our point of departure the case-based position, where: causation is viewed in deterministic and asymmetric terms, the focus is ensuring causal homogeneity in case-based research to enable cross-case inferences to be made, and finally where mechanisms are understood as more than just intervening variables but instead a system of interacting parts that transfers causal forces from causes to outcomes. We then develop a set of case selection guidelines that are in methodological alignment with these underlying assumptions. We then develop guidelines for research where the mechanism is the primary focus, contending that only typical cases where both X, Y, and the requisite contextual conditions are present should be selected. We compare our guidelines with the existing, finding that practices like selecting most/least-likely cases are not compatible with the underlying assumptions of tracing mechanisms. We then present guidelines for deviant cases, focusing on tracing mechanisms until they breakdown as a tool to shed light on omitted contextual and/or causal conditions.