The claim assumes correlation and causation are distinct categories, but in well-specified statistical models, correlation structure directly reflects causal architecture of inheritance.

?Rate how convincing each reason is below to see the overall strength.

No one has weighed in yet. Be the first to share reasons for or against this statement.

Inheritance(the relationship that requires asymmetric dependence): In philosophy, the idea that specific properties get their characteristics from more general categories—like how a specific shade of red inherits features from the general concept of color.
Well-specified statistical models(as used in statistics and philosophy of science): Mathematical formulas that are carefully designed with all the right variables included to accurately represent how something actually works in the real world.
causal architecture(explaining how different types of causation might work): The underlying structure or system that explains *how* something causes something else—the different mechanisms or pathways through which effects happen.
causation(Lewis's counterfactual theory of causation): Event C causes event E if and only if there exists a chain C, D1, …, Dn, E such that each member (except C) is counterfactually dependent on the preceding event; causation is the ancestral of counterfactual dependence

Based on where you are in your exploration