Causal Identification Boundary for Consciousness-Relevant Organization

Abstract

This architecture cannot definitively establish the presence of phenomenal experience. Rather, its operational objective is to map the causal pathways of complex information processing to determine if H_C (Consciousness-relevant causal organization) can be mathematically separated from H_0 (Advanced Stochastic Parrotism). The underlying methodology relies strictly on structural equation models (SEMs), Bayesian networks, and directed acyclic graphs (DAGs) operating via Judea Pearl's do-calculus, alongside information-theoretic signatures (mutual information, transfer entropy). The final output is an empirical causal graph mapping information routing—not a proof of sentience. LIMITATIONS, FAILURE MODES, AND ALTERNATIVE HYPOTHESES (H0): The causal identification architecture is limited to observational data and does not include direct manipulation of phenomenal experience. Failure modes include: 1. Causal Sufficiency: Unobserved confounders may exist. Mitigation: Instrumental variables and sensitivity analyses. 2. Measurement Error: Neural measures such as wPLI are noisy. Mitigation: Multimodal validation and repeated measures. 3. Model Misspecification: The DAG may not capture the true causal structure. Mitigation: Bayesian model averaging and alternative DAG comparison. 4. Countermodel Equivalence: A non-conscious countermodel may produce similar causal separation. Mitigation: Mandatory comparison with countermodel and locked AUROC margin.