Boost Your Thinking.
Transform Your Life

Subscribe to Thinking Hacks, our free weekly newsletter now.

Boost Your Thinking.
Transform Your Life

Subscribe to Thinking Hacks, our free weekly newsletter now.

Boost Your Thinking.
Transform Your Life

Subscribe to Thinking Hacks, our free weekly newsletter now.

Mental Models
Featured Models

Correlation vs Causation

The release of Nicolas Cage movies makes more people drown in pools! I know, that’s a big claim, but I have the data to prove it. Information from the Centre for Disease Control & Prevention clearly demonstrates that the number of people who drown in pools is linked to Nicolas Cage movie releases. Actually, that might be a timely point to introduce this model...  Correlation is a positive or negative mirroring of statistical results between two variables, causation identifies a link or ‘cause and effect’ relationship between those variables. Importantly, correlation does not imply causation.  THE CAUSAL FALLACY.  An understanding of this model helps to interrupt the causal fallacy, also known as the questionable cause, where you falsely find meaning in chance. Specifically, you attribute causal relationships to correlated factors. It’s understandable, your desire to see patterns and create narratives as you attempt to find order and explain the world is admirable. However, it’s also important to view assumed cause and effect relationships as one hypothesis, and remain open to other options.    AN EXAMPLE AS EASY AS A, B, C. For example, you might notice a correlation between A and B. Both A and B might move or grow in complete alignment (positive correlation) or, as A grows, B might diminish at a similar rate (negative correlation). At that point, you might consider several logical hypotheses:  A causes B. B causes A. A and B are linked or reinforce each other.  C, a third factor known as a confounder, causes both A and B.  The pattern between A and B is a coincidence.  There is a combination of some of the above factors at play.  IN YOUR LATTICEWORK. Correlation versus causation is a fundamental model for Data Science and unconscious biases in Behavioural Economics.  The Causal Fallacy is common when initial extreme variations are subject to Regression to the Mean and can be influenced by the Confirmation Heuristic. It highlights the need to apply the Scientific Method to challenge and test your hypothesis. It also helps to use Probabilistic Thinking to have a more complex, flexible view of what is happening and why. Also, consider using the 5 Whys or the Fishbone Diagram to explore true causal relationships.  View the Actionable Takeaways below for some strategies to accurately identify cause and effect.

Explore Mental Models

What are Mental Models?
Categories:

Subcategories:

No Mental Model Available

Related content