Resource allocation method improves treatment assignment with levels and priorities
Precedence-Aware Resource Allocation: Extending the AUTOC Framework to Multi-Level Treatments
Machine LearningArtificial Intelligence
Summary
Allocating limited resources for treatments can be tricky when some treatments need earlier ones done first. The authors created a way to measure how well these stepped treatments work when deciding who gets what. They extended an existing evaluation method to handle many treatment levels with order restrictions and showed that simply giving the biggest treatments to the highest benefit people is not always best. Their approach helps assign treatments more wisely under budget limits.
What this means in practice
- •For healthcare administrators: Optimize patient care by allocating multi-step treatments efficiently under budget limits using precedence-aware policies.
- •For social service program managers: Assign graduated intervention levels to beneficiaries considering order requirements and fixed budgets for better overall impact.
Authors
Olin E. Gilster, Shengpu Tang
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.