ML-AUTOC：将 AUTOC 框架扩展到带优先级约束的多层级处理资源分配
Precedence-Aware Resource Allocation: Extending the AUTOC Framework to Multi-Level Treatments
AI 导读
arXiv 论文提出 Multi-Level AUTOC（ML-AUTOC），将原本仅适用于二值处理的 AUTOC 评估框架推广到带优先级约束的多层级处理场景，并设计了在固定预算下将真实边际处理效应映射为可行处理分配的 precedence-aware 策略。研究显示，在多层级处理设置中最大化实际处理效应可能需要在个体间重新分配处理资源。
正文 · 原文
Abstract:Many real-world decision problems require allocating limited resources across individuals to maximize total benefit often through interventions that vary in intensity rather than binary treat-or-not decisions. In these settings, higher levels treatments require first applying lower levels of treatment, inducing a precedence constraint that complicates allocation under a fixed budget. While the causal inference literature has largely emphasized accurate estimation of conditional average treatment effects (CATE), recent work has shown that estimation accuracy alone does not guarantee effective resource allocation, motivating ranking-based evaluation frameworks. The Area Under the Targeting Operating Characteristic (AUTOC) provides an effective way to evaluate prioritization rules across budget levels, but existing formulations are limited to binary treatments. We introduce Multi-Level AUTOC (ML-AUTOC), which is a generalization for AUTOC designed for multi-level treatment settings with precedence. We also propose precedence-aware policies for mapping true marginal treatment effects to feasible treatment assignments under fixed budget constraints. Our results show that in the setting of multi-level treatments, maximizing realized treatment effects may require re-allocation of treatments across individuals.
| Subjects: | Machine Learning (cs.LG); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2610.34812 [cs.LG] |
| (or arXiv:2610.34812v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2610.34812 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Shengpu Tang [view email]
[v1]
Mon, 26 Oct 2026 03:57:24 UTC (125 KB)
来源：arXiv cs.AI · arxiv.org