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Time-Sensitive RCTs in Behavioural Public Policy: A Pragmatic Framework Using Sequence Methods, Personalisation, and Reinforcement Learning
Frontiers in Behavioral Economics
This article proposes a pragmatic framework for analysing behavioural RCTs through sequence methods and Markov modelling, capturing how interventions unfold over time. Rather than relying on average outcomes alone, the approach tracks individual transitions between behavioural states, producing decision-oriented outputs such as transition probabilities, expected persistence durations, and trajectory clusters. The framework extends to personalised interventions and reinforcement learning, and is operationalised through sequenceRCT, an open R package. Applications include booster scheduling, resource triage, and programme exit criteria for behavioural policy.
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Veltri, G.A.(2026). Time-Sensitive RCTs in Behavioural Public Policy: A Pragmatic Framework Using Sequence Methods, Personalisation, and Reinforcement Learning. Frontiers in Behavioral Economics. In press. DOI
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