Verification can confirm patterns, but it doesn’t establish causation—shared context often hides missing variables, so proper experimental design is still essential.
Thanks Laura - though the paper uses "non-causal" in a different sense than causal inference in statistics. Here it means the verification relation itself is structurally non-causal: the verifier cannot affect what it verifies without collapsing the distinction it exists to draw. Worth a read if you're curious about the architectural side.
Laura Ashaley
Bioinformatics & Data Science | Home Decor Design
Verification can confirm patterns, but it doesn’t establish causation—shared context often hides missing variables, so proper experimental design is still essential.