How exactly does MICE imputation combine multiple datasets into one? I'm trying to understand Multiple Imputation with Chained Equation (MICE) imputation process (a statistical method for imputing missing data). I have read some articles and I have understood how the imputation happens, but I didn't get the &quot;pooling&quot; step.
After analyzing the resulting datasets with Rubin's rules, how to pool these datasets? How to get only one dataset?
In the end, do I combine all these datasets? If yes, how? Or do I compare every dataset's estimators with Rubin's estimators and choose one dataset?
MICE is a Multiple imputation method, it contains three phases:

Imputation phase: The missing data are estimated M times from a specific model to obtain M complete and potentially different data sets.
Separate analysis phase: The selected analysis is performed separately on each of m = 1, ..., M sets of imputed data to obtain estimates (central tendency and variance).
Combined analysis phase: The results obtained  are combined according to the rules established by Rubin to obtain a single final estimate.