The Great LIE - Correlation is Not Causation - FutureIQ
Concepts in this episode
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Spurious Correlation mental-model
Two variables can trace strikingly similar paths without either causing the other. A matched curve is a reason to investigate, not a causal explanation.
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Correlation vs Causation principle
Observed association is evidence to investigate, not proof of causal power. A credible causal claim needs comparison cases and a mechanism that can distinguish causation from confounding, reverse direction, and coincidence.
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Confounding Variable mechanism
A hidden third factor can produce both variables, creating a correlation without a direct causal link. Causal analysis must test plausible common causes before drawing an arrow from one observation to the other.
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Milton Friedman's Thermostat mental-model
When a controller actively holds an outcome steady, ordinary correlations can hide—or appear to reverse—the forces acting on it. Stability may be evidence of continuous intervention, not causal independence.
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Causal Mechanism principle
A causal claim needs a plausible sequence showing how one thing changes another. Without that mechanism, the same correlation can accommodate incompatible explanations.
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Reverse Causality mechanism
An observed relationship can be genuine while its causal arrow points backward: what appears to be X producing Y may instead be Y producing X.
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Bidirectional Causality mechanism
Some relationships carry causal force in both directions: each variable can intensify the other, creating a feedback loop that one-way explanations miss.
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