A central question in economics is almost always, “how does X affect Y?”. We look not merely for relationships between variables (as X increases, Y falls), but seek causal explanations (the fall in Y is a result of the increase in X). Unfortunately for this kind of econometric analysis, the world is complex. We seldom can control the settings we observe; many variables can be changing simultaneously. Proving causality in economics typically requires techniques that can isolate the effect of the specific relationship of interest.
An example to illustrate why this might be so: it is common knowledge that, on average, those who study at university earn higher incomes than those who left school at the first possible opportunity. So, education causes higher wages for those with education? Perhaps. But it is also possible — likely even — that if all universities closed tomorrow, and everyone left school at the same age, that (again, on average) those who would have gone to university might still have better career prospects and earn a higher income than those who would never have studied further if given the chance. That is, there is some other factor at play beyond education itself, which influences 1) the likelihood of someone pursuing education and 2) later-life employment outcomes.→