Selecting and Ranking Individualized Treatment Rules With Unmeasured Confounding

It is common to compare individualized treatment rules based on the value function, which is the expected potential outcome under the treatment rule. Although the value function is not point-identified when there is unmeasured confounding, it still defines a partial order among the treatment rules under Rosenbaum's sensitivity analysis model. We first consider how to compare two treatment rules with unmeasured confounding in the single-decision setting and then use this pairwise test to rank multiple treatment rules. We consider how to, among many treatment rules, select the best rules and select the rules that are better than a control rule. The proposed methods are illustrated using two real examples, one about the benefit of malaria prevention programs to different age groups and another about the effect of late retirement on senior health in different gender and occupation groups. for this article are available online.

Automated summary

This article discusses the comparison of individualized treatment rules based on the value function and explores decision-making in the presence of unmeasured confounding factors. By comparing two treatment rules in a single-decision environment and utilizing this test to rank multiple treatment rules, the study aims to select the best rules among many options.