1.23 Correlation, causation and confounding
You can say when a model's finding supports an action.
Before:00. Orientation & SetupUnlocks:03. Data Handling & Analysis04. Classical AI — Agents, Search & Knowledge Representation
Models find correlations; decisions need causes, and this topic is the bridge to cross before recommending action from any model. Confounders and Simpson's paradox show how a true correlation can point the wrong way entirely, and causal graphs give a notation for thinking it through. It sits near the end of statistics because it keeps everything the models later produce honest. The confusion that costs money is assuming a strong predictor is a lever — a variable can predict beautifully and move nothing when pushed.
Work through these
Spurious correlation and Simpson's paradox
Two quantities can move together for reasons that have nothing to do with each other, and aggregated data can reverse a relationship present in every subgroup. Both are common and both are counter-intuitive.
Confounders, colliders, mediators
Three kinds of third variable, each of which requires opposite handling: one you must adjust for, one you must not, and one that sits on the path itself. Getting this backwards produces confident nonsense.
Causal graphs (DAGs) as a thinking tool
Drawing the assumed causal structure as a diagram makes it possible to reason about what may be adjusted for. It is a thinking tool rather than a statistical procedure.
Natural experiments, IV, difference-in-differences
When an experiment is impossible, certain natural situations approximate one, and there are standard designs for exploiting them. These are how causal questions get answered outside a laboratory.
Sign in to keep your progress.
Free resources
We haven't checked most of these for screen reader use yet.
Links last checked 29 Aug 2026.
Stuck here?
Ask a mentor. A real person answers, and they can see exactly which topic you're on. Usually within a couple of working days.
Checking your session…
Topics shown in module order.