Moving Toward Best Practice When Using Propensity Score Weighting in Survey Observational Studies

Propensity score weighting is a common method for estimating treatment effects with observational data, by addressing confounding due to measured baseline covariates. However, when the observational data sample is drawn based on a survey, the existing literature does not reach a consensus on the optimal use of survey weights for population-level causal inference. Under the balancing weights framework, we provide a unified solution for incorporating survey weights and derive a set of weighting and augmented weighting estimators for different target populations, including the combined, treated, controlled, and overlap populations. We also develop closed-form sandwich variance estimators for each estimator via the theory of M-estimators. Through an extensive series of simulation studies, we examined the performance of our estimators and compared the results to those of alternative methods. We carried out two case studies to illustrate the application of the different propensity score methods with complex survey data. We concluded with a discussion of our findings and provided some practical recommendations for propensity score weighting analysis of survey observational data.