From Metaphor to Identification: A Causal Architecture for the Extended Phenotype Theory of Law

Evolutionary accounts of law frequently use causal language while leaving the relevant causal questions underspecified. This article develops the Dynamic Extended-Phenotype Causal Architecture (DEPCA), a structural framework for converting the Extended Phenotype Theory of Law from a mechanism-rich research program into an identification-ready one. DEPCA represents legal replicators, institutional configurations, agent behavior, social outcomes, selective environments, and subsequent replicator retention as time-indexed variables in a dynamic structural causal model. Six propositions distinguish temporal feedback, institutional causal roles, measurement from causation, causal from intentional ladders, persistence from efficacy, and causal attribution from legal responsibility. Applications to constitutional lock-in, AI liability, and channel opacity show how the architecture changes research design and limits permissible claims. The contribution is theoretical and methodological; it does not claim that any particular legal intervention has already been causally identified. Epistemic boundary. DEPCA is a theoretical and methodological architecture. The paper reports no simulation and estimates no causal effect. Its applications are worked specifications and research designs, not empirical findings.