Individual Treatment Prescription Effect Estimation in a Low Compliance Setting

TL;DR

This paper introduces a novel method for estimating individual treatment effects in low compliance scenarios by leveraging observed compliance data, using causal modeling to improve accuracy over existing methods.

Contribution

It proposes a new estimator within the Structural Causal Model framework that effectively recovers individual treatment effects despite low compliance, with proven asymptotic guarantees.

Findings

The method outperforms state-of-the-art in synthetic datasets.

It demonstrates improved accuracy in real-world health and advertising data.

The approach maintains consistency and asymptotic efficiency.

Abstract

Individual Treatment Effect (ITE) estimation is an extensively researched problem, with applications in various domains. We model the case where there exists heterogeneous non-compliance to a randomly assigned treatment, a typical situation in health (because of non-compliance to prescription) or digital advertising (because of competition and ad blockers for instance). The lower the compliance, the more the effect of treatment prescription, or individual prescription effect (IPE), signal fades away and becomes hard to estimate. We propose a new approach for the estimation of the IPE that takes advantage of observed compliance information to prevent signal fading. Using the Structural Causal Model framework and do-calculus, we define a general mediated causal effect setting and propose a corresponding estimator which consistently recovers the IPE with asymptotic variance guarantees. Finally, we conduct experiments on both synthetic and real-world datasets that highlight the benefit of the approach, which consistently improves state-of-the-art in low compliance settings