Learning Treatment Effects during Resource Allocation via Priority-Queue Randomization
通过优先队列随机化在资源分配中学习处理效应AI 解读 ↓
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通过优先队列随机化在资源分配中学习处理效应
- 研究问题
- 当公共服务依照需求优先级和预算分批分配资源时，如何在照顾高需求申请人的同时识别处理效应，并兼顾评估效率？
- 方法与思路
- 作者按风险评分将申请人随机分入优先队列，按队列优先级及队内先到先得顺序提供处理，并优化队列分配设计。
- 材料陈述的贡献
- 摘要称，外生到达时可识别常规处理效应，内生到达时队列随机化可作为工具变量，并支持以通常的独立同分布效率界优化设计。
- 阅读关注点
- 应核对到达过程外生与内生的具体定义、条件随机化和工具变量识别条件，以及队列依赖如何影响效率界的适用性。
- 阅读边界
- 仅据 arXiv 预印本摘要，不能确认同行评审或期刊接收；具体识别条件、效率论证及住房分配应用的结果未说明。
原文摘要
Public service programs often allocate limited resources under uncertainty about their benefits, creating a need for randomization to support credible evaluation. In practice, however, applicants commonly enter waitlists where resources are prioritized toward individuals judged to have higher need through tiered priority queues, making direct randomization difficult. Motivated by this, we develop an experimental design framework for learning treatment effects while treating those most in need where incoming applicants are randomized into priority queues based on their assessed risk scores. Treatments are then provided across queues in priority order and first-in-first-out within queue as budget becomes available. Our contributions are two-fold. First, we characterize what causal effects are identified under this priority-queue allocation. When arrivals are exogenous, treatments are conditionally randomized, and hence standard estimands are identified; when arrivals are endogenous, queue randomization instead provides an instrument for treatment, identifying local treatment effects induced by the queuing process. Second, we develop optimized queue-assignment designs that trade off statistical efficiency against prioritizing higher-need applicants. We show in the process that, despite dependence in treatment assignments induced by the design, usual iid efficiency bounds remain well-justified design objectives. We illustrate the proposed designs using data from a housing allocation program in a large U.S. county.