Why Correlation Heatmaps Miss Multicollinearity in Regression Models

A common practice in regression analysis involves using pairwise correlation heatmaps to detect multicollinearity, but this method has a fundamental blind spot. Multicollinearity can exist across three or more features simultaneously even when no individual pair exceeds the correlation threshold, such as when one variable is the sum of two others. The Variance Inflation Factor (VIF) is a more reliable diagnostic because it regresses each feature against all others, capturing multivariate dependencies that pairwise statistics cannot detect. A high VIF signals that a coefficient's variance is inflated, making it statistically unstable and potentially misleading for causal or policy-driven interpretation. Crucially, multicollinearity only poses a real problem when the goal is to interpret coefficients rather than generate predictions, so the appropriate response depends on whether the model is being used for explanation or forecasting.