A doubly robust Q-learning framework with path-specific inverse probability weights learns cost-optimal sequential testing policies under sequential missing-at-random, delivering theoretical guarantees and improved performance over baselines in simulations and a prostate cancer cohort.
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Cost-optimal Sequential Testing via Doubly Robust Q-learning
A doubly robust Q-learning framework with path-specific inverse probability weights learns cost-optimal sequential testing policies under sequential missing-at-random, delivering theoretical guarantees and improved performance over baselines in simulations and a prostate cancer cohort.