Analytical Explainability
Drivers vs Correlations: Explaining Without Overclaiming
Drivers explain causes; correlations only show association. AI must distinguish them.
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Observed change can lead to correlation or driver evidence.
TL;DR
- • Drivers imply causality; correlations do not.
- • AI should report both clearly.
The problem (layman)
- • AI explanations sometimes overstate correlation as causation.
- • Business users misinterpret patterns.
Why it matters
- • Overclaiming leads to bad decisions.
- • Trust depends on careful explanation.
Symptoms
- • AI states “X caused Y” without evidence.
- • Explanations change when a correlated factor shifts.
Root causes
- • No driver measures or causal context.
- • Lack of explanation guidelines.
What good looks like
- • Explanations clearly label drivers vs correlations.
- • AI outputs include caveats.
How to fix (steps)
- • Define driver measures with business logic.
- • Add caveats for correlation‑only insights.
- • Include confidence levels in narratives.
Pitfalls
- • Assuming any correlation is a driver.
- • Omitting uncertainty.
Checklist
- • Drivers defined for key KPIs.
- • Correlation language standardized.
- • Caveats included in explanations.