What is the primary risk of using MICE (Multivariate Imputation by Chained Equations) on a dataset with high multicollinearity?

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MICE works by iteratively regressing each missing variable against all other variables. If features are highly collinear, the design matrix becomes near-singular or singular, making the underlying regression models (like OLS) unstable or impossible to solve. This leads to convergence failures or extreme, nonsensical imputed values. Regularization (e.g., Ridge) is often required to stabilize MICE in the presence of multicollinearity.