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
Verification can confirm patterns, but it doesn’t establish causation—shared context often hides missing variables, so proper experimental design is still essential.