• Formalization and testing of higher-order interactions as a structural signature of dependence. From zero second-order interactions in 3-way tables to general higher-order testing, these works unify criteria and likelihood-based tests for multi-way dependency patterns [1], [2], [4], [19].

• Path-based causal modeling and econometric frameworks for social science data. The lineage includes path analysis, dependence analysis, and causal-chain econometrics, illustrating how linear causal models are built, tested, and interpreted in sociology and economics [7], [15], [18], [16].

• Robustness of causal inference under measurement error and extraneous variation. These papers examine how random measurement error, spurious variables, and reliability affect inferred causal relations, emphasizing explicit modeling of error components [8], [12], [13].

• Conditional independence, partial association, and data-driven causal pattern detection. The work on partial associations, conditional independence tests, and empirical pattern discernment lays a foundation for drawing causal claims from observed correlations [20], [11], [5].

• Foundational causality and explanatory frameworks across domains. Cross-disciplinary treatments of causality—ranging from physics-based causality in Lorentzian structures to philosophical/logical treatments of explanation—shape how causal reasoning is framed in data analysis [3], [14], [6].