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Correlation implies a suspicion of causation. Also ML teaches us that assuming "correlation implies causation" works fairly well in practice if you're looking at sufficiently many potential correlations over a large enough dataset.
ML models are based 100% on correlation, and they do manage to generalize nicely (with significant limitations)
#ML models ok to use correlation when they’re making _predictions_. But the correlation vs causation paradoxes occur when we are trying to assess _intervening_.
A can predict B if:
A => B
B => A or
C => A and C => B
But (change A) => (change B) iff A => B
@yudapearl
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