I answered this question which provides R code for your case after using mice to multiply impute, MatchIt to match within each imputed data set, and glm() to estimate treatment effects in each imputed data set.

See the documentation for cobalt for an example of the other method (averaging propensity scores across imputations).

Update 1/4/20: A new package has been written for this purpose called MatchThem. It's on CRAN, and an article about how to use it is under review. It's compatible with version 4.0.0 of cobalt for balance checking. It has built-in functions for performing matching and estimating treatment effects from multiply imputed data. It has integration with svyglm() and svycoxph(), so you can estimate treatment effects for various forms of outcome. It really smooths out the process of estimating effects from multiply imputed data.

I answered this question which provides R code for your case after using mice to multiply impute, MatchIt to match within each imputed data set, and glm() to estimate treatment effects in each imputed data set.

See the documentation for cobalt for an example of the other method (averaging propensity scores across imputations).

In answered this question which provides R code for your case after using mice to multiply impute, MatchIt to match within each imputed data set, and glm() to estimate treatment effects in each imputed data set.

See the documentation for cobalt for an example of the other method (averaging propensity scores across imputations).

I answered this question which provides R code for your case after using mice to multiply impute, MatchIt to match within each imputed data set, and glm() to estimate treatment effects in each imputed data set.

See the documentation for cobalt for an example of the other method (averaging propensity scores across imputations).