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Abstract:
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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. We propose two new 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). 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 for the control (ATC). We demonstrate the properties of the proposed estimators through theoretical proof and simulation studies. We also applied our proposed methods in a comparison of insulin and glucagon-like peptide-1 receptor agonists therapies among patients with type 2 diabetes from the UK clinical practice research datalink (CPRD) data.
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