Statistical models such as longitudinal studies or most econometric techniques draw causal inference from the statistical relationship between cause and effect or between variables. Theory-based approaches include causal process designs that build on the identification of causal processes or causal pathways (e.g. process tracing or contribution analysis) and causal mechanism designs that consider supporting factors and causal mechanisms (such as the realist evaluation paradigm or congruence analysis). Case-based approaches include grounded theory or ethnographic approaches and rely mainly on within-case analysis. Cross-case analysis of several case studies can be managed by configurational approaches such as qualitative comparative analysis (QCA) with the analytic generalisation based on theory. Thereby, integrated approaches are able to combine exploratory and explanatory approaches in sequence or in parallel, nested or balanced with different conceptual frameworks to causal inference. Thanks to their integrative character mixed- or multi-method approaches are also open for new data types and analytical approaches such as geographic data, big data or text mining.

This article argues that no methodological approach is best or even sufficient on its own.