Document Type : Research Paper

Author

• Ismail Aalizad

Associate Professor of Sociology, Faculty of Social Sciences, Allameh Tabataba'i University.

Abstract

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 and consider it to be linear, symmetric, and largely probabilistic, which limits the ability to answer “under what conditions” questions. In contrast, set-based causality logic, by focusing on necessary and sufficient relationships, allows for the analysis of causal combinations, multipaths, and asymmetries of relationships. By recognizing empirical heterogeneity and being sensitive to degree differences between cases, this logic strengthens the link between theory, conceptualization, and empirical analysis. The FsQCA framework is introduced as an operational formulation of this logic that uses fuzzy sets to enable the simultaneous analysis of necessary conditions and sufficient combinations. However, its successful application requires a clear theoretical framework, precise conceptual calibration, and correct definition of sets. The paper shows that the transition from linear to mixed causality has important implications for research questions, generalizability, and explanatory validity, and can lead to the production of more realistic and theoretically richer explanations in social research.

Keywords

• Combinatorial Causality
• Asymmetric Causality
• Set-Oriented Logic
• Fuzzy Algebra
• FsQCA
• Fuzzy Calibration

Main Subjects

• Methodology of Social Sciences