  Optimal treatment rules are mappings from individual patient characteristics
to tailored treatment assignments that maximize mean outcomes. In this work, we
introduce a conditional potential benefit (CPB) metric that measures the
expected improvement under an optimally chosen treatment compared to the status
quo, within covariate strata. The potential benefit combines (i) the magnitude
of the treatment effect, and (ii) the propensity for subjects to naturally
select a suboptimal treatment. As a consequence, heterogeneity in the CPB can
provide key insights into the mechanism by which a treatment acts and/or
highlight potential barriers to treatment access or adverse effects. Moreover,
we demonstrate that CPB is the natural prioritization score for individualized
treatment policies when intervention capacity is constrained. That is, in the
resource-limited setting where treatment options are freely accessible, but the
ability to intervene on a portion of the target population is constrained
(e.g., if the population is large, and follow-up and encouragement of treatment
uptake is labor-intensive), targeting subjects with highest CPB maximizes the
mean outcome. Focusing on this resource-limited setting, we derive formulas for
optimal constrained treatment rules, and for any given budget, quantify the
loss compared to the optimal unconstrained rule. We describe sufficient
identification assumptions, and propose nonparametric, robust, and efficient
estimators of the proposed quantities emerging from our framework.
