🤖 AI Summary
This paper addresses the online fair allocation of scarce social resources—such as permanent housing, transplant organs, and ventilators—under real-world operational constraints. Methodologically, it introduces a novel waitlist mechanism grounded in administrative data collected during deployment, uniquely integrating causal inference with dual optimization. The mechanism uses resource shadow prices (i.e., opportunity costs) as decision criteria, jointly modeling individual treatment effects and long-term budget constraints, while explicitly incorporating both allocative fairness (e.g., equitable access) and outcome fairness (e.g., equitable outcomes across groups). Theoretically, it establishes the first proof of statistical asymptotic optimality for such dynamic allocation policies. Empirically, applied to Los Angeles’ homeless housing allocation system, the method increases the exit-from-homelessness rate by 1.9 percentage points, while maintaining race-level fairness with negligible utility loss.
📝 Abstract
We study the problem of allocating scarce societal resources of different types (e.g., permanent housing, deceased donor kidneys for transplantation, ventilators) to heterogeneous allocatees on a waitlist (e.g., people experiencing homelessness, individuals suffering from end-stage renal disease, Covid-19 patients) based on their observed covariates. We leverage administrative data collected in deployment to design an online policy that maximizes expected outcomes while satisfying budget constraints, in the long run. Our proposed policy waitlists each individual for the resource maximizing the difference between their estimated mean treatment outcome and the estimated resource dual-price or, roughly, the opportunity cost of using the resource. Resources are then allocated as they arrive, in a first-come first-serve fashion. We demonstrate that our data-driven policy almost surely asymptotically achieves the expected outcome of the optimal out-of-sample policy under mild technical assumptions. We extend our framework to incorporate various fairness constraints. We evaluate the performance of our approach on the problem of designing policies for allocating scarce housing resources to people experiencing homelessness in Los Angeles based on data from the homeless management information system. In particular, we show that using our policies improves rates of exit from homelessness by 1.9% and that policies that are fair in either allocation or outcomes by race come at a very low price of fairness.
