The term “confounding” originally meant “mixing” in English, and we can understand from the diagram why this name was chosen. The true causal effect X → Y is “mixed” with the spurious correlation between X and Y induced by the fork X ← Z → Y. For example, if we are testing a drug and give it to patients who are younger on average than the people in the control group, then age becomes a confounder—a lurking third variable. If we don’t have any data on the ages, we will not be able to disentangle the true effect from the spurious effect.
