Individual Treatment Prescription Effect Estimation in a Low Compliance Setting
This work addresses the challenge of accurate treatment effect estimation for practitioners in fields like health and advertising where non-compliance is common, representing an incremental improvement over existing methods.
The paper tackles the problem of estimating individual prescription effects (IPE) in settings with low compliance to randomly assigned treatments, such as healthcare or digital advertising, by proposing a new estimator that leverages observed compliance information to prevent signal fading. The approach consistently improves state-of-the-art results in low compliance settings, as demonstrated in experiments on synthetic and real-world datasets.
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