How can scientists determine cause and effect when experiments are impossible? A new framework published in The Innovation uses delay embedding entropy to infer intervention-level causality directly from observational data, eliminating hidden confounders in neural circuits and complex systems.
How can scientists determine cause and effect when experiments are impossible? A new framework published in The Innovation uses delay embedding entropy to infer intervention-level causality directly from observational data, eliminating hidden confounders in neural circuits and complex systems.
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