Case Selection and the Validity of Causal Inferences in Qualitative Comparative Research
Traditionally, social scientists perceived causality as regularity. As a consequence, qualitative comparative research was regarded as unsuitable for drawing causal inferences since few cases cannot establish regularity. The dominant perception of causality has changed, however. Nowadays, social scientists define and identify causality through the counterfactual effect of a treatment. This brings causal inference in qualitative comparative research back on the agenda. We argue that the validity of causal inferences from the comparative study of cases depends on the case selection algorithm. We employ Monte Carlo techniques to demonstrate that different case selection rules strongly differ in their ex ante reliability for making valid causal inferences.