MatchThem:: Matching and Weighting after Multiple Imputation

Balancing the distributions of the confounders across the exposure levels in observational study through matching or weighting is an accepted method to for confounding due to these variables when estimating the association an exposure and outcome and to reduce the degree of dependence on modeling assumptions. Despite the increasing popularity in practice, procedures cannot be immediately applied to datasets with missing values. imputation of the missing data is a popular approach to account for values while preserving the number of units in the dataset and for the uncertainty in the missing values. However, to the best of knowledge, there is no comprehensive matching and weighting software that be easily implemented with multiply imputed datasets. In this paper, we this problem and suggest a framework to map out the matching and multiply imputed datasets to 5 actions as well as the best practices assess balance in these datasets after matching and weighting. We also these approaches using a companion package for R, MatchThem.