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If the confounders are fat-tail distributed etc. then arbitrarily large samples can still be inadequate.

The idea that even thousands a data points in subgroups are going to be 'well mixed' relies on extremely strong assumptions about the distribution of those traits.



Just assumptions, not extremely strong assumptions. Ordinary least squares performs well in a large variety of cases.




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