Time-sensitive RCTs in behavioral public policy: a pragmatic framework using sequence methods, personalization, and reinforcement learning

This article presents a pragmatic framework for time-sensitive analysis of behavioral RCTs using sequence methods and Markov modeling. The focus is not methodological novelty but translation: we map common policy questions to appropriate temporal tools, provide a reporting checklist for transparency, and show how estimates become implementable rules for booster timing, triage, and exit. We position sequence analysis alongside multi-state hazards, HMMs, SMART/MRT, and g-methods, and we introduce