Many research studies aim to draw causal inferences using data from large, nationally representative survey samples, and many of these studies use propensity score matching to make those causal inferences as rigorous as possible given the non-experimental nature of the data. However, very few applied studies are careful about incorporating the survey design with the propensity score analysis, which may mean that the results don’t generate population inferences. This may be because few methodological studies examine how to best combine these methods. Furthermore, even fewer of the methodological studies incorporate different non-response mechanisms in their analysis. This study examines methods for how to handle survey weights in propensity score matching analyses of survey data, under diferent non-response mechanisms. Based on the results from Monte Carlo simulations implemented on synthetic data as well as a data based application we developed suggestions regarding the implementation of propensity score methods to make causal inferences relevant to the target population of a sample survey. Our main conclusions are: (1) whether the survey weights are incorporated in the estimation of the propensity score does not impact estimation of the population treatment effect, as long as good population balance is achieved across confounders, (2) survey weights must be taken into account in the outcome analysis and (3) transfer of survey weights (i.e., matched comparison units are assigned the sampling weight of the treated unit they have been matched to) can be benefcial under certain non-response mechanisms.


Biostatistics | Statistical Methodology

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