Correlation versus Causation
The distinction between two variables moving together (such as lighter AI regulation and faster AI-sector job growth in the same states) and one variable actually causing the other; a report using a cross-state or cross-country comparison to imply a policy's causal effect must address credible alternative explanations for the correlation, including a confounding variable, a third factor driving both sides of the correlation at once.
Defined in 2 GAGE programs, which carry 2 distinct definitions of it. The wording above is taught in The AI Lobbyist: Certified AI Policy Strategist.
How each discipline defines it
The same term does different work depending on who is using it. These are the definitions as each program teaches them, unedited.
The distinction between two variables moving together (such as lighter AI regulation and faster AI-sector job growth in the same states) and one variable actually causing the other; a report using a cross-state or cross-country comparison to imply a policy's causal effect must address credible alternative explanations for the correlation, including a confounding variable, a third factor driving both sides of the correlation at once.
The distinction between two things occurring together (correlation) and one causing the other (causation); a core data-literacy concept.
Where it is taught
The exact lessons this term appears in. The first 7 topics of every program are free with a free account.
- Interconnected Literacies: How Digital, Data, and AI Literacy Connect · Digital Foundations, Certified AI Practitioner: Workplace Foundations
- The Economic Impact Report with Numbers That Survive Scrutiny · AI Economics and Impact Modeling, The AI Lobbyist: Certified AI Policy Strategist
Terms it appears with
Not an alphabetical neighbourhood: these are the terms taught in the same lessons, ranked by how often they appear together.