Causal inference with limited resources: proportionally-representative interventions

TL;DR

This paper introduces a causal inference method for evaluating treatment effects under resource constraints, using proportionally-representative interventions and inverse probability weighting, demonstrated on liver transplant data.

Contribution

It proposes a new estimand and simple inverse probability weighted estimators for causal effects with limited resources, addressing a gap in treatment effect evaluation.

Findings

Developed a new estimand for resource-constrained treatment strategies.

Derived inverse probability weighted estimators for the proposed estimand.

Applied the method to liver transplant resource allocation data.

Abstract

Investigators often evaluate treatment effects by considering settings in which all individuals are assigned a treatment of interest, assuming that an unlimited number of treatment units are available. However, many real-life treatments are of limited supply and cannot be provided to all individuals in the population. For example, patients on the liver transplant waiting list cannot be assigned a liver transplant immediately at the time they reach highest priority because a suitable organ is not likely to be immediately available. In these cases, investigators may still be interested in the effects of treatment strategies in which a finite number of organs are available at a given time, that is, treatment regimes that satisfy resource constraints. Here, we describe an estimand that can be used to define causal effects of treatment strategies that satisfy resource constraints: proportionally-representative interventions for limited resources. We derive a simple class of inverse probability weighted estimators, and apply one such estimator to evaluate the effect of restricting or expanding utilization of "increased risk" liver organs to treat patients with end-stage liver disease. Our method is designed to evaluate policy-relevant interventions in the setting of finite treatment resources.