The analytical method for extracting predictive patterns from engagement record data: which maintenance interventions cause which posture outcomes? Causal modeling transforms descriptive engagement data into prescriptive recommendations. Example causal questions: Does tier-1 corroboration frequency predict schema entropy rate? Does L1a verification gate failure predict subsequent citation coverage degradation? Causal modeling requires structured engagement records, bi-temporal provenance, and sufficient sample size. The causal model is the analytical asset that makes the data moat actionable — converting proprietary data into proprietary insight.