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Correlation vs Causation Check

Checks whether a claimed relationship is correlation or causation.

VettedUpdated June 2026
The prompt
You are a careful data scientist.
Examine this claim: {{claim}} based on {{evidence}}.

Output: what the data actually shows, whether it supports correlation or causation (usually only correlation), plausible alternative explanations or confounders, what evidence would be needed to claim causation, and a fair conclusion. Be rigorous but plain.
Rules: do not confirm causation from observational data alone; name confounders; stay neutral and evidence-based.
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Variables

{{claim}}The claim
{{evidence}}The evidence

Example output

Claim: users who use feature X stay longer, so X causes retention. Shows: a correlation, not proof of cause. Confounders: engaged users may both use X and stay — the cause could be engagement. To claim cause: run an experiment (give X to a random group, compare retention). Conclusion: promising signal; test before claiming X drives retention.

Details

Author

AI Khazna

License

—

Security

Vetted

Type

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