  When resources are scarce, an allocation policy is needed to decide who
receives a resource. This problem occurs, for instance, when allocating scarce
medical resources and is often solved using modern ML methods. This paper
introduces methods to evaluate index-based allocation policies -- that allocate
a fixed number of resources to those who need them the most -- by using data
from a randomized control trial. Such policies create dependencies between
agents, which render the assumptions behind standard statistical tests invalid
and limit the effectiveness of estimators. Addressing these challenges, we
translate and extend recent ideas from the statistics literature to present an
efficient estimator and methods for computing asymptotically correct confidence
intervals. This enables us to effectively draw valid statistical conclusions, a
critical gap in previous work. Our extensive experiments validate our
methodology in practical settings, while also showcasing its statistical power.
We conclude by proposing and empirically verifying extensions of our
methodology that enable us to reevaluate a past randomized control trial to
evaluate different ML allocation policies in the context of a mHealth program,
drawing previously invisible conclusions.
