One-step weighting to generalize and transport treatment effect estimates to a target population
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Weighting methods are often used to generalize and transport estimates of causal effects from a study sample to a target population. Traditional methods construct the weights by separately modeling the treatment assignment and the study selection probabilities and then multiplying functions (e.g., inverses) of the estimated probabilities. These estimated multiplicative weights may not produce adequate covariate balance and can be highly variable, resulting in biased and/or unstable estimators, particularly when there is limited covariate overlap across populations or treatment groups. To address these limitations, we propose a weighting approach for both randomized and observational studies that weights each treatment group directly in `one go' towards the target population. We present a general framework for generalization and transportation by characterizing the study and target populations in terms of generic probability distributions. Under this framework, we justify this one-step weighting approach. By construction, this approach directly balances covariates relative to the target population and produces weights that are stable. Moreover, in some settings, this approach does not require individual-level data from the target population. We connect this approach to inverse probability and inverse odds weighting. We show that the one-step weighting estimator for the target average treatment effect is consistent, asymptotically Normal, doubly-robust, and semiparametrically efficient. We demonstrate the performance of this approach using a simulation study and a randomized case study on the effects of physician racial diversity on preventive healthcare utilization among Black men in California.
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