This article examines the limitations of variable-based approaches to the analysis of complex and contextual phenomena by rethinking the logic of causality in the social sciences. Variable-based approaches define causality primarily in terms of correlations or statistical effects between variables; as a result, causality is often assumed to be probabilistic, linear, and symmetric, and limited to answering “under what conditions” questions. In contrast, set-based logic redefines causality in terms of necessary and sufficient relations, allowing for the analysis of causal combinations, multipath ways, and asymmetries of relationships. By recognizing empirical heterogeneity and maintaining sensitivity to degree differences between cases, this approach strengthens the link between theory, conceptualization, and empirical analysis. The methodological framework FsQCA is introduced as an operational embodiment of this set-based logic. Using fuzzy sets, FsQCA allows for the simultaneous analysis of necessary conditions and sufficient combinations, examining causal multipaths, and confronting causal asymmetries. However, the success of this approach depends on a clear theoretical framework, precise conceptual calibration, and correct definition of sets and conditions, and reducing it to a software tool can reduce its explanatory capacity. The paper also analyzes the methodological implications of the transition from linear and symmetric causality to combined and asymmetric causality. This transition not only redefines research questions and the role of theory, but also transforms the concept of generalization and explanatory validity in multi-layered and complex social analysis. Finally, the paper argues that improving the quality of social research requires, above all, a rethinking of the logic of explanation, and that FsQCA, within the theoretical framework and given its limitations, allows for the production of more realistic and theoretically richer explanations.