Table 3. Mapping of causal propositions to empirical strategies

Proposition	Methodology	Data source	Measurable outcomes
P1	Randomized controlled trial	Experimental group vs. control	Composite working memory score (average standardized results from 2-back and 3-back tasks), attentional control measures, and long-term durability markers
P2	Agent-based modeling and field study	Simulated environments and BAI users	Error rate per unit time and task throughput (completion time adjusted for accuracy)
P3	Quasi-experiment (difference-in-differences)	Organizational-level panel data	Proportion of participants retaining their role over 12 months, wage progression
P4	Computable general equilibrium simulation (CGE)	National economic data	Change in GDP (ΔGDP) and fiscal balance from the CGE simulation

BAI, brain–artificial intelligence interfaces; GDP, gross domestic product.
These methodological approaches align with the causal logic of each proposition. Randomized controlled trials and longitudinal cognitive testing are suited for assessing the neurocognitive outcomes in P1. Agent-based modeling combined with real-world task data provides evidence for the interactional dynamics in P2. P3 requires organizational-level longitudinal data to isolate employment effects of BAI adoption, while P4 uses macroeconomic modeling to capture aggregate fiscal and growth impacts. Together, these methods form a multi-level empirical roadmap for validating the theoretical framework.