Why the best in-sample fit gives you the worst causal answers
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Why the best in-sample fit gives you the worst causal answers
Causal inference and prediction are different games. In media mix modeling (and other causal work), the goal isn’t the best in-sample fit, but the best estimate of a specific parameter of interest (e.g., the incremental effect of a channel). That’s why models with higher R² or lower in-sample error often deliver worse causal answers. Here ...
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