从观测数据中学习资源分配策略及其在无家可归者服务提供中的应用
Learning Resource Allocation Policies from Observational Data with an Application to Homeless Services Delivery
摘要：
我们研究学习的问题，从观察数据，公平和可解释的政策，有效地匹配异质个体的不同类型的稀缺资源。我们将此问题建模为一个多类多服务器排队系统，其中个人和资源随时间随机到达。每个个体在到达时被分配到一个队列中，在那里他们等待与资源匹配。根据对服务于每个队列的资源类型进行编码的资格结构，以先到先服务（FCFS）的方式分配资源。我们提出了一种基于现代因果推理技术的方法来构建个人队列以及学习匹配结果，并提供了一个混合整数优化（MIO）配方来优化资格结构。MIO问题是在等待时间和公平性约束下最大化策略结果的问题。它非常灵活，允许额外的线性域约束。我们使用合成和真实世界的数据进行广泛的分析。特别是，我们使用美国无家可归者管理信息系统（HMIS）的数据来评估我们的框架。我们获得了与FCFS政策一样低的等待时间，同时提高了服务不足或弱势群体的无家可归率（黑人高出7%，17岁以下高出15%）和整体。
英文摘要：
We study the problem of learning, from observational data, fair and interpretable policies that effectively match heterogeneous individuals to scarce resources of different types. We model this problem as a multi-class multi-server queuing system where both individuals and resources arrive stochastically over time. Each individual, upon arrival, is assigned to a queue where they wait to be matched to a resource. The resources are assigned in a first come first served (FCFS) fashion according to an eligibility structure that encodes the resource types that serve each queue. We propose a methodology based on techniques in modern causal inference to construct the individual queues as well as learn the matching outcomes and provide a mixed-integer optimization (MIO) formulation to optimize the eligibility structure. The MIO problem maximizes policy outcome subject to wait time and fairness constraints. It is very flexible, allowing for additional linear domain constraints. We conduct extensive analyses using synthetic and real-world data. In particular, we evaluate our framework using data from the U.S. Homeless Management Information System (HMIS). We obtain wait times as low as an FCFS policy while improving the rate of exit from homelessness for underserved or vulnerable groups (7% higher for the Black individuals and 15% higher for those below 17 years old) and overall.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
