Learning Treatment Effects during Resource Allocation via Priority-Queue Randomization

2026-05-24

AI Summary

This study addresses the challenge of balancing priority-based allocation for high-need individuals with reliable causal inference in public resource distribution. The authors propose a priority-queue randomized experimental design in which applicants are randomly assigned to queues based on risk scores, and resources are allocated according to budget constraints, priority rankings, and first-come-first-served rules within each queue. This framework is the first to characterize identifiable causal effects under queue-based mechanisms, integrating instrumental variables, conditional randomization, and efficiency bound analysis under non-i.i.d. sampling. It identifies local average treatment effects under endogenous arrival processes and recovers standard estimators when arrivals are exogenous. Empirical validation using housing allocation data demonstrates the approach’s ability to effectively trade off statistical efficiency against fairness guarantees.

Abstract

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.

Problem

• treatment effects
• resource allocation
• priority queues
• randomization
• causal inference

Innovation

• priority-queue randomization
• treatment effect estimation
• causal inference
• resource allocation
• instrumental variable