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arXiv · 2604.11165

Cost-optimal Sequential Testing via Doubly Robust Q-learning

Doudou Zhou·Yiran Zhang·Dian Jin·Yingye Zheng·Lu Tian·Tianxi Cai
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Abstract

Clinical decision-making often involves selecting tests that are costly, invasive, or time-consuming, motivating individualized, sequential strategies for what to measure and when to stop ascertaining. We study the problem of learning cost-optimal sequential decision policies from retrospective data, where test availability depends on prior results, inducing informative missingness. Under a sequential missing-at-random mechanism, we develop a doubly robust Q-learning framework for estimating optimal policies. The method introduces path-specific inverse probability weights that account for heterogeneous test trajectories and satisfy a normalization property conditional on the observed history. By combining these weights with auxiliary contrast models, we construct orthogonal pseudo-outcomes that enable unbiased policy learning when either the acquisition model or the contrast model is correctly specified. We establish oracle inequalities for the stage-wise contrast estimators, along with convergence rates, regret bounds, and misclassification rates for the learned policy. Simulations demonstrate improved cost-adjusted performance over weighted and complete-case baselines, and an application to a prostate cancer cohort study illustrates how the method reduces testing cost without compromising predictive accuracy.

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Doudou Zhou, Yiran Zhang, Dian Jin, Yingye Zheng, Lu Tian, Tianxi Cai. 2026-04-15. Cost-optimal Sequential Testing via Doubly Robust Q-learning. https://arxiv.org/abs/2604.11165

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