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It's just a special case of omitted variables with categorial variables. So instead of parameter estimates being biased up or down x amount (to the extent covariates are correlated with error terms), with Simpsons's paradox the mean effect is completely wrong due to improper grouping. This often leads to flipping signs on estimated parameters -- 'surprising' results that gets papers published.

My favourite explanation: http://vudlab.com/simpsons/

You're correct. It isn't different. Simpsons paradox is actually a key indicator of a confounding variable.

The more complicated examples of Simpson's paradox tend to be important causes being ignored. But it's not always an issue of causality, like in the example of two Wikipedia contributors. That example doesn't really have a hidden cause, it's just the use of percentages where total articles is clearly the more useful metric.