QCA: learning from cases

Causal inference in case-based methods like quantitative comparative analysis (QCA) is a dialogue between singular (case-level) and general (cross-case) evidence. QCA uses the truth table to dialogue both by connecting meaning (case-level knowledge) to Boolean expressions (cross-case patterns). Using knowledge of cases, context and concepts, researchers interpret Boolean expressions into plausible causal explanations. The substantive plausibility of these explanations is more important than the robustness of the empirical patterns from which they are interpreted. Critical realist philosophy of science legitimizes and informs QCAs interpretive logic; it demonstrates how events in the domain of the Actual are interpreted into analytically relevant cases; how researchers develop (case-level and cross-case) knowledge of cases in the domain of the Empirical; and how this knowledge is interpreted into claims about causal powers in the domain of the Real. Critical realism is thus an important heuristic for QCA.