arXiv:2605.25169v1 Announce Type: new 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
The increasing sophistication of AI and data analysis methods is enabling more nuanced and ethical approaches to resource allocation in public services.
This development allows for improved, data-driven decision-making in public service programs, potentially leading to more effective and equitable outcomes for beneficiaries.
Traditional direct randomization in public services can now be adapted through priority-queue systems, allowing for both ethical resource allocation and robust treatment effect evaluation.
- · Public service programs
- · Social scientists and evaluators
- · Beneficiaries of public services
- · AI/ML ethics researchers
- · Inefficient resource allocation strategies
- · Traditional, less adaptable randomization methods
More precise understanding of treatment effects in social programs without compromising ethical considerations for those most in need.
Increased adoption of AI and machine learning in public sector operational design and policy evaluation, driven by the ability to manage ethical constraints.
Improved public trust and legitimacy for evaluative research in sensitive social programs, fostering a cycle of data-driven policy refinement.
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