Cost-optimal Sequential Testing via Doubly Robust Q-learning
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 hist...
| Authors | Doudou Zhou, Yiran Zhang, Dian Jin et al. |
|---|---|
| Primary category | stat.ML |
| Published | 13 April 2026 |
| Last updated at source | 15 April 2026 |
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