Jeson Clarke

Understanding the Difference Between Correlation and Causation in Data

In data analysis circles, you'll hear the terms"correlation" and "causation" thrown around pretty frequently. While the two are similar, these words have vastly different meanings. Understanding how they differ can prevent you from wasting effort and resources on factors that don't impact the bigger picture.

The Basics of Correlation and Causation in Data Analysis

First things first, let's define these concepts.

Causation is about pinpointing the direct cause of an outcome. It's explicit and applies to cases where action A directly causes outcome B. There's no question, and the link is clear. In data analysis, finding the causation is a common goal. When you can link one outcome to a specific action directly, it's possible to recreate the results and improve your product or service.

Now, the correlation is a bit different. Causation and correlation can exist simultaneously, but correlation doesn't explicitly imply causation. That's because correlation is only a relationship between two actions.

For example, action A can have a relationship to action B. However, that doesn't necessarily mean that action A causes action B.

The Issue with Implied Causation

Many people confuse correlation and causation. It's not hard to see why when you understand how the human brain works. The brain works overtime to identify patterns and establish a relationship between two variables. When we see that two actions are related somehow, we automatically jump to a "cause and effect" relationship.

We're hard-wired to build those connections and establish patterns. However, correlation and causation in data analysis have to look past the patterns we create in our heads. Our observations are anecdotal, and there are many other potential causes for a relationship.

For example, action B could cause action A, not the other way around. There could also be an outside variable involved. In that case, action A could only cause action B if action C occurred.

Those are just a few examples, but they highlight how the correlation between two variables doesn't indicate direct causation.

Correlation and causation seem like simple concepts at first. But when you're trying to make sense of complex data, your mind can play tricks on you and force you to build connections that don't exist.

Author Resource:-

Jeson Clarke writes about technologies, import/export data and customs data tools. You can find his thoughts at data analytics platform blog.