  Warfarin, an anticoagulant medication, is formulated to prevent and address
conditions associated with abnormal blood clotting, making it one of the most
prescribed drugs globally. However, determining the suitable dosage remains
challenging due to individual response variations, and prescribing an incorrect
dosage may lead to severe consequences. Contextual bandit and reinforcement
learning have shown promise in addressing this issue. Given the wide
availability of observational data and safety concerns of decision-making in
healthcare, we focused on using exclusively observational data from historical
policies as demonstrations to derive new policies; we utilized offline policy
learning and evaluation in a contextual bandit setting to establish the optimal
personalized dosage strategy. Our learned policies surpassed these baseline
approaches without genotype inputs, even when given a suboptimal demonstration,
showcasing promising application potential.
