Stata multiple-imputation reference manual

TL;DR: A Stata implementation of coarsened exact matching, a new method for improving the estimation of causal effects by reducing imbalance in covariates between treated and control groups, is introduced.

TL;DR: Choice of method for dealing with missing data is crucial for validity of conclusions, and should be based on careful consideration of the reasons for the missing data, missing data patterns and the availability of auxiliary information.

TL;DR: The mcartest command is introduced, which implements Little's missing completely at random test and its extension for testing the covariate-dependent missingness, and includes an option to perform the likelihood-ratio test with adjustment for unequal variances.

TL;DR: The ice package for multiple imputation by chained equations (also known as fully conditional specification) is further updated, with special attention paid to categorical variables.