The rule in one line

Correlation does not imply causation means that two things moving together do not prove that one makes the other happen.

Why the rule matters

1. Effects can share a hidden cause. Two variables may rise together because a third factor drives both.
2. Timing can mislead. If A follows B, the order alone does not prove B caused A.
3. Chance can mimic patterns. With enough data, some pairs will correlate by luck.
4. Measurement choices can distort results. Biased samples or noisy instruments create false links.

How to apply the rule in practice

1. Ask the causal question explicitly
   What is the proposed mechanism from X to Y
2. Search for confounders
   List factors that could influence both X and Y. Age, season, income, selection criteria, and context are frequent culprits.
3. Check direction
   Could Y be causing X instead of the reverse
4. Test robustness
   Does the relationship persist after adjusting for obvious confounders
5. Seek natural experiments or experiments
   Randomize when ethical and feasible. If not, use quasi experimental designs such as difference in differences, instrumental variables, or regression discontinuity.
6. Look for dose response and timing fit
   More cause should predict more effect, and the effect should appear after the cause, not before.
7. Demand converging evidence
   Combine statistical controls, theory, and replication across settings.

Why the rule works

• Logic
  Evidence that X and Y move together does not exclude alternative explanations. Without ruling out alternatives, causation is underdetermined.
• Probability
  Random variation creates spurious correlations, especially with many variables.
• Mechanism
  Causal claims require a plausible pathway from cause to effect. Correlation alone says nothing about such a pathway.

Everyday life examples

• Health
  People who carry lighters get more lung disease. The lighter does not cause disease. Smoking is the confounder.
• Education
  Schools with higher test scores spend more on music. Money on music is likely a marker for overall funding or parental income, not the direct cause of higher scores.
• Fitness
  New running shoes appear right before a performance plateau. The shoes did not cause the slowdown. Overtraining or a change in routine may explain both.
• Weather and sales
  Ice cream sales and drowning incidents rise together in summer. Heat increases both, not ice cream causing drowning.

Quick field tests you can run

• The flip test
  If reversing cause and effect still sounds plausible, correlation is not enough.
• The other cause test
  Can you name two reasonable confounders in under 30 seconds
• The dose test
  Do increments in X align with increments in Y in a way a mechanism would predict
• The timing test
  Does X consistently precede Y

Tools that help separate correlation from causation

• Randomized trials
  Random assignment balances hidden factors on average, so differences can be attributed to the treatment.
• Statistical adjustment
  Multiple regression, matching, and stratification can reduce bias from measured confounders.
• Natural experiments
  Policy cutoffs, lotteries, and sudden rule changes can approximate randomization.
• Replication and triangulation
  Repeating results across methods and contexts increases confidence that a link is causal.

Common traps to avoid

• Cherry picking
  Selecting only supportive slices of data inflates false links.
• Overcontrolling
  Adjusting for mediators can hide real effects. Control for confounders, not for variables on the pathway from cause to effect.
• P hacking
  Trying many analyses until something looks significant produces illusory correlations. Pre register when possible.

How to communicate this rule clearly

• Use plain statements
  We found a correlation between X and Y. We have not shown that X causes Y.
• Offer next steps
  We will test dose response, control for Z, and attempt a small randomized trial.
• Provide visual checks
  Time plots, scatterplots with trend lines, and subgroup comparisons can reveal confounding patterns.

When correlation can be a clue

Correlation is a starting signal. Treat it as a hypothesis generator, not a verdict. Strong, repeated correlations that survive adjustments and match a credible mechanism deserve deeper causal testing.

A mini checklist for decisions

• What mechanism could link X to Y
• What confounders could drive both
• Does the timing fit
• Do adjustments change the result
• Do independent studies agree
• Have you tested an intervention that would change X and observed Y respond

Bottom line

Correlation is useful for spotting patterns and forming questions. Causation demands more. When you insist on mechanisms, controls, timing, and tests, you turn interesting coincidences into reliable knowledge that you can act on with confidence.