difficulty 3/5
Confounding
A third variable that causes both the thing you changed and the thing you measured. Adjusting for the right one rescues an estimate; adjusting for the wrong one invents an effect that is not there.
Intuition
Downey states the problem in one sentence: "If variables A and B are correlated, there are three possible explanations: A causes B, or B causes A, or some other set of factors causes both A and B" [1, §7.8]. And then the consequence: "Correlation alone does not distinguish between these explanations, so it does not tell you which ones are true" [1, §7.8].
The third explanation is confounding. scikit-learn gives the textbook case in a working example: a model that predicts wage from education. "A confounding variable is a variable that causes both EDUCATION and WAGE. One example of such variable is ability" [2, Wrong causal interpretation].
What makes this worth a page rather than a slogan is that the usual remedy — put the third variable in the model — is right in one case and catastrophic in another, and no amount of looking at the data will tell you which case you are in. That is not a limitation of your tools. It is a property of the question.
Formal definition
How it works
Where it's used
Why & when
Worked example
Pitfalls
Check yourself
3 questions
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Sources
- Think Stats, 2nd ed.book · retrieved 2026-09-18https://greenteapress.com/thinkstats2/html/thinkstats2008.html
- scikit-learn — Common pitfalls in the interpretation of coefficients of linear modelsdocs · retrieved 2026-09-18https://scikit-learn.org/stable/auto_examples/inspection/plot_linear_model_coefficient_interpretation.html