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.

稀缺资源分配政策的评估，采用随机对照试验数据，通过翻译和扩展最近的统计学思想提出了一种有效的估计方法和计算渐近正确置信区间的方法，验证其在实际环境中的方法学，提供先前不可见的结论。

评估基于指标的治疗分配的效果

Evaluating the Effectiveness of Index-Based Treatment Allocation

Scheduling is the task of assigning a set of scarce resources distributed
over time to a set of agents, who typically have preferences about the
assignments they would like to get. Due to the constrained nature of these
problems, satisfying all agents' preferences is often infeasible, which might
lead to some agents not being happy with the resulting schedule. Providing
explanations has been shown to increase satisfaction and trust in solutions
produced by AI tools. However, it is particularly challenging to explain
solutions that are influenced by and impact on multiple agents. In this paper
we introduce the EXPRES framework, which can explain why a given preference was
unsatisfied in a given optimal schedule. The EXPRES framework consists of: (i)
an explanation generator that, based on a Mixed-Integer Linear Programming
model, finds the best set of reasons that can explain an unsatisfied
preference; and (ii) an explanation parser, which translates the generated
explanations into human interpretable ones. Through simulations, we show that
the explanation generator can efficiently scale to large instances. Finally,
through a set of user studies within J.P. Morgan, we show that employees
preferred the explanations generated by EXPRES over human-generated ones when
considering workforce scheduling scenarios.

本文介绍了一种基于混合整数线性规划模型的 “EXPRES 框架”，可解释资源调度方案中未能满足某些参与者首选项的原因，并且该框架通过在 J.P. Morgan 内部的用户研究中表明，当考虑员工排班情景时，EXPRES 产生的解释被员工比人工生成的解释更喜欢。