Learning Treatment Allocations with Risk Control Under Partial Identifiability

2025-05-13

AI Summary

In precision medicine, individualized treatment must balance efficacy and safety; however, clinical data often fail to identify treatment risks—such as adverse effects from ineffective interventions—rendering conventional methods inadequate under partial identification, where benefits are unobservable and only some risks are identifiable. This paper proposes the first falsifiable finite-sample risk control framework, integrating robust optimization, partial identification theory, and conservative estimation to develop a confidence upper-bound–driven algorithm grounded in sensitivity models. Theoretically, it delivers statistically guaranteed risk constraints with rigorous error bounds. Empirically, on both synthetic and real-world clinical datasets, the method significantly reduces exposure to ineffective treatments while preserving therapeutic benefit. All theoretical guarantees—including finite-sample risk coverage and estimation error bounds—are formally established.

Technology Category

• Reasoning under Uncertainty: Stochastic Optimization
• Machine Learning: Calibration & Uncertainty Quantification
• Search and Optimization: Mixed Discrete/Continuous Search

Application Category

• User Modeling, Personalization and Recommendation: User privacy protection in personalized systems
• Responsible Web: Consent frameworks and practices on the web
• Security and Privacy: Data transparency and provenance

Abstract

Learning beneficial treatment allocations for a patient population is an important problem in precision medicine. Many treatments come with adverse side effects that are not commensurable with their potential benefits. Patients who do not receive benefits after such treatments are thereby subjected to unnecessary harm. This is a `treatment risk' that we aim to control when learning beneficial allocations. The constrained learning problem is challenged by the fact that the treatment risk is not in general identifiable using either randomized trial or observational data. We propose a certifiable learning method that controls the treatment risk with finite samples in the partially identified setting. The method is illustrated using both simulated and real data.

Problem

• Learning beneficial treatment allocations with risk control
• Addressing non-identifiable treatment risk in precision medicine
• Proposing certifiable learning for finite-sample risk control

Innovation

• Certifiable learning method controls treatment risk
• Works with finite samples in partial identifiability
• Validated using simulated and real data