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

Researchers have developed a doubly robust Q-learning framework for learning cost-optimal sequential testing policies from retrospective clinical data, addressing the challenge of selecting which tests to order and when to stop given that test availability depends on prior results. The method handles informative missingness through path-specific inverse probability weights and auxiliary contrast models, enabling unbiased policy learning when either the acquisition or contrast model is correctly specified. Simulations and a prostate cancer cohort application show the approach reduces testing costs without sacrificing predictive accuracy compared to weighted and complete-case baselines.