Performing multivariate imputation by chained equations

Multivariate imputation methods, as opposed to univariate imputation, use multiple variables to estimate the missing values. In other words, the missing values of a variable are modeled based on the other variables in the dataset. Multivariate Imputation by Chained Equations (MICE) models each variable with missing values as a function of the remaining variables and uses that estimate for imputation.

The following steps are required to perform MICE:

1. A simple univariate imputation is performed for every variable with missing data, for example, median imputation.
2. One specific variable is selected, say, var_1, and the missing values are set back to missing.
3. A model is trained to predict var_1 using the remaining variables as input features.
4. The missing values of var_1 are replaced with the new estimates.
5. Steps 2 to 4 are repeated for each of the remaining variables.

Once all the variables have been...