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