KB article
Drivers vs Correlations: Explaining Without Overclaiming
Drivers explain causes; correlations only show association. AI must distinguish them.
arf-kbanalytical-explainabilitydriversexplainabilityassumptions-and-caveats
TL;DR
- Drivers imply causality; correlations do not.
- AI should report both clearly.
The problem
- 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
- 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.
Framework placement
Primary ARF layer: Analytical Explainability. Diagnostic bridge: semantic-reliability, execution-reliability, change-reliability.