Suppose some causal relationship between some measurable phenomenon A and some measurable phenomenon B, through some undetermined positive feedback loop (respectively a constant statistical proportion, or some other simple mathematical construct). But how much does A depend on B? Well, do it scientifically, by computing with lots of experimentally measured numbers! You put some historical numbers in, you fit your model to those postdictions, and ask your simulation to make predictions about the future. What do you get? An exponential future growth of A due to B (respectively, a proportional growth of A, or whatever follows your causal law). And what happens if you force B to be higher or lower in the future? Then the growth of A correspondingly goes faster or slower. This establishes a clear dependency of A to B, doesn't it?
Well, yes, of course — it establishes the dependency you introduced in the model, to begin with. The positive feedback loop is a conclusion of the model only because it was inserted as an explicit premise of the model. To wave around your model with lots of numbers and graphics as a proof that something alarming is happening and that production of B should be stopped (respectively started), is a fallacy: it is a petition of principle hidden under a lot of verbiage, it is intimidation using the usurped authority of "official truth", it is confusion of the layman by a mass of numbers and complex maths, it is numerology dressed up as science.
Unhappily, that kind of pseudo-science is what is taught in universities and used as the basis for public policy, in such domains as economics, sociology, and as of late, climatology. And it is pseudo-science precisely because it is used as the basis for public policy, in a positive feedback loop where government officials fund and establish intellectuals who justify their power.
Next time you're shown a model, skip all the numbers, all the mathematical formulas, the graphics, the computed projections, the "scientific" conclusions, and the policy recommendations. Ask what were the hypotheses on which the model resides; what are the relationships assumed between various phenomena, and why are these relationship assumed not to move in general, and in particular when the recommended policy changes the system?
More often than not, you'll find that the general conclusion was already assumed as a hypothesis to the model, and that the whole model is a swindle to smuggle the premise as if it had somehow been established by the model.
Sure, some keynesian economist will show you some beautiful equation, with aggregates that add apples and oranges, whose multiplicative relationship to some government-controlled variable is magically supposed to be constant, unlike simpler variables. "This equation is true by definition!" will he even claim. Sure. Just like it's true by definition that being hit by a magic long sword +2 will cause you 1d8+2 of hit point damage. That's also the definition, straight from the rulebook. The definition is tautologically true in the corresponding model. Now whether the model accurately describes reality, that's quite a different question. At least the D&D rulebook lets me have fun with friends and doesn't serve as a pretext to massive armed robbery.