Toward a Statistical Framework for Structural Indivisibility: Rethinking Causal Inference in Complex Institutional Systems

This paper introduces the concept of structural indivisibility as a theoretical and methodological challenge to conventional approaches in causal inference, particularly within complex institutional systems. Drawing on insights from institutional theory, philosophy of science, and statistical modeling, the study argues that certain institutional variables—such as policy roadmaps, administrative capacity, and institutional trust—may form co-constitutive configurations that resist decomposition into independent causal components. Through a combination of theoretical analysis and empirical illustration using OECD data, the paper demonstrates how conventional regression-based methods may obscure rather than clarify causal relationships in such contexts. The study proposes a shift from decomposition to structural recognition, advocating for alternative modeling strategies that preserve the integrity of relational configurations. Implications for policy evaluation, comparative institutional analysis, and future methodological development are discussed.