Imagine you see people carrying umbrellas, and it often rains on those days.
Any human understands perfectly that umbrellas don't cause rain, while
traditional AI might think umbrellas cause rain, even if it is not true. This is
the difference between correlation and causation.
Judea Pearl's Ladder of Causality has three levels: association (observing correlations), intervention (predicting effects of actions), and counterfactuals (imagining alternate outcomes). It's important because it shows that true causal understanding goes beyond data patterns, enabling reasoning about change and "what if" scenarios—essential for science, decision-making, and artificial intelligence.