Learning Treatment Allocations with Risk Control Under Partial Identifiability

TL;DR

This paper introduces a certifiable method for learning treatment allocations that controls treatment risk in precision medicine, even when the risk is only partially identifiable from data, demonstrated through simulations and real data.

Contribution

It proposes a novel learning approach that guarantees treatment risk control under partial identifiability, addressing a key challenge in personalized treatment decision-making.

Findings

Method effectively controls treatment risk with finite samples.

Demonstrated success on simulated data.

Validated on real-world data.

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.