Robust Estimation of the Weighted Average Treatment Effect for A Target Population

TL;DR

This paper introduces two robust estimators for the weighted average treatment effect tailored to a specific target population, using observational data and augmented inverse probability weighting, with theoretical validation and real-world application.

Contribution

It proposes novel doubly robust estimators for WATE that accommodate known or linear-dependent target functions, enhancing causal inference in observational studies.

Findings

Estimators are doubly robust under specified conditions.

The methods perform well in simulations.

Application to diabetes treatments demonstrates practical utility.

Abstract

The weighted average treatment effect (WATE) is a causal measure for the comparison of interventions in a specific target population, which may be different from the population where data are sampled from. For instance, when the goal is to introduce a new treatment to a target population, the question is what efficacy (or effectiveness) can be gained by switching patients from a standard of care (control) to this new treatment, for which the average treatment effect for the control (ATC) estimand can be applied. In this paper, we propose two estimators based on augmented inverse probability weighting to estimate the WATE for a well defined target population (i.e., there exists a target function that describes the population of interest), using observational data. The first proposed estimator is doubly robust if the target function is known or can be correctly specified. The second proposed estimator is doubly robust if the target function has a linear dependence on the propensity score, which can be used to estimate the average treatment effect for the treated (ATT) and ATC. We demonstrate the properties of the proposed estimators through theoretical proof and simulation studies. We also apply our proposed methods in a comparison of glucagon-like peptide-1 receptor agonists therapy and insulin therapy among patients with type 2 diabetes, using the UK clinical practice research datalink data.