1. Three sources of correlation
When "A and B always occur together", there are at least three explanations:
- A causes B — the direction you want to prove.
- B causes A — the arrow reverses. "People who exercise catch fewer colds" may also be "healthier people find it easier to keep exercising".
- C causes both — a common factor. Ice-cream sales and drowning deaths both rise with summer.
And a fourth: plain coincidence, especially with small samples or short time windows.
2. Why we jump to causation
- Narrative preference — causal stories are more memorable than statistics, so the brain fills in a plot.
- Temporal adjacency — things that happen one after another feel causal, but sequence is not cause.
- Confirmation bias — if you want a conclusion, you remember the supporting cases.
3. Five criteria for a causal claim
- Temporal order — the cause must precede the effect.
- Strength and stability — does the relationship hold across samples and settings?
- Excluding common causes — could a third factor explain both?
- Dose–response — more exposure, stronger effect, makes causation more credible.
- Intervention — change the cause and watch whether the effect follows. This is the strongest evidence.
Any single criterion is not enough; they are thresholds to be met together.
4. Using it during inference
Whenever you write "because X, therefore Y", do three things: ask whether the arrow could reverse; ask whether a third cause exists; ask whether changing X would change Y. Answer all three and the causal claim earns its weight.
These are exactly the checks Ockham applies in its causal-reasoning step. Related: base-rate fallacy, Bayesian thinking, abductive reasoning.