Introduction

In the previous chapters, we used a regression-based approach for case selection. Like any approach, relying on regression has important trade-offs that researchers need to be aware of. Perhaps most importantly, this approach relies on the functional form assumptions of the underlying regression analysis, which researchers may not want to adopt in selecting cases for pathway analysis. The good news is that there is nothing about pathway analysis that requires the use of regression analysis. We have concentrated on regression analysis because it is well known by researchers, and we think likely to be used in practice. We recognize that there are a host of other options to leverage existing large-N data to select cases for comparative pathway analysis.

This chapter illustrates one alternative to regression-based case selection. It explores how a researcher might use a common matching approach to select cases, and we apply the technique to an example we also discuss in the next chapter: the relationship between natural resource exports and civil conflict. A matching approach is useful, because it is explicitly designed for making comparisons. This is valuable in our context, because it fits well with the underlying importance of comparative research strategies. It is worth noting that others have also combined matching approaches with case studies, but the goal in prior approaches has been to understand causal effects (Nielsen 2014; Rosenbaum and Silber 2001); in general, matching approaches aimed at improving causal inference focus on identifying comparable treatment and control cases based on pretreatment co-variates that are related to treatment (Ho et al. 2007; Rubin 2008). In pathway analysis, however, the goal differs. By definition, pathway analysis assumes that the causal effect has been identified in previous scholarship and matching is used as a means to identify cases that are appropriate for exploring the mechanisms underlying the previously identified relationship.