What is the primary consequence of applying MICE (Multivariate Imputation by Chained Equations) to a dataset with high multicollinearity?

Question

Data Science — Hard

What is the primary consequence of applying MICE (Multivariate Imputation by Chained Equations) to a dataset with high multicollinearity?

Accepted Answer

MICE relies on regression models to predict missing values based on other features. If features are highly multicollinear, the regression coefficients in the chained equations become unstable, leading to erratic and biased imputations. While singular matrices (D) can occur, instability (A) is the more common and insidious consequence in practice. Mean convergence (B) is characteristic of simple mean imputation, not MICE.