A Visualization: Correlation vs Causation

There are a lot of charts floating around, discussing how x is related to y because they are “highly correlated”.

Our brains tend to make nonsensical connections to try and explain the world to us. We draw two lines and claim a relationship, thinking that we have effectively answered the question in the process.

Not quite.

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Source: Etsy

Correlation is not causation.

Correlation is a relationship, in which A moves with B. There are three types:

  • Positive: A and B increase/decrease at the same time
  • Negative: increase (decrease) in A leads to a decrease (increase) in B
  • None: A and B are unrelated

Causation is a cause-and-effect, in which A causes B.

I’ve made a couple of graphs to illustrate the fine line between correlation and causation — and how correlation can insinuate a relationship that might not actually exist.

Correlation versus Causation

Are falling emissions levels impacting Kim Kardashians popularity?

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Are McDonalds restaurants causing inflation?

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Did Chads drive the fall in interest rates?

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Did Twitter users increase Tesla’s share price?

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Is the price of bitcoin a leading indicator for avocado sales?

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Did farmers stop farming because of the success of the Marvel Movies?

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This is an illustration to show that just because data series move together, doesn’t mean that they cause each other — or that they are “related” at all.

Most of these examples show datasets that have no direct relationship to each other — but they look like they do.

Correlation does not necessarily mean causation.

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