Individualized treatment rules under stochastic treatment cost constraints

TL;DR

This paper develops methods to optimize individualized treatment rules considering stochastic treatment costs and resource constraints, addressing a gap in existing literature.

Contribution

It introduces a novel approach to estimate optimal treatment rules under random cost constraints and constructs an efficient estimator for treatment effects.

Findings

Proposed a new method for treatment rule estimation under cost constraints.

Developed an asymptotically efficient estimator for treatment effects.

Addresses treatment resource limitations in personalized medicine.

Abstract

Estimation and evaluation of individualized treatment rules have been studied extensively, but real-world treatment resource constraints have received limited attention in existing methods. We investigate a setting in which treatment is intervened upon based on covariates to optimize the mean counterfactual outcome under treatment cost constraints when the treatment cost is random. In a particularly interesting special case, an instrumental variable corresponding to encouragement to treatment is intervened upon with constraints on the proportion receiving treatment. For such settings, we first develop a method to estimate optimal individualized treatment rules. We further construct an asymptotically efficient plug-in estimator of the corresponding average treatment effect relative to a given reference rule.