Correlation vs. Causation: Key Differences Explained


The Difference between Correlation and Causation predictive dialers
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When we look at data and research, it’s important to know the difference between correlation and causation. These are two big words that mean different things but are often mixed up. In this blog post, we’ll talk about what they mean, give examples, and explain why knowing the difference is so important.

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What is a correlation?

In analytics, correlation shows a connection between variables. When one changes, the other follows suit. It is important to understand that this connection doesn’t always suggest a direct or indirect cause between the variables. It’s important to understand that the relationship between variables doesn’t always mean one causes the other. 

there are three main types of correlations between variables: 

1. Positive Correlation: When variables have a positive correlation, it means they move in the same direction. When x increases, y increases, and when x decreases, y decreases. 

2. Negative Correlation: When variables have a negative correlation, it means they move in opposite directions. When x increases, y decreases, and when x decreases, y increases. 

3. No Correlation: When variables have no correlation, it means there is no relationship between them. When x increases, y either stays the same or has no distinct pattern. 

Take, for instance, the following example: When temperatures rise in the summer, both ice cream sales and swimsuit sales increase. Despite the positive correlation between ice cream and swimsuit sales, there’s no causal link between these variables. Rather, the rise in temperature separately influences both of these variables, resulting in their positive correlation.

What is causation?

In analytics, causation refers to a relationship where one variable is dependent on the other, indicating a causal relationship. In a causal relationship, there is an independent variable (‘the cause’) and a dependent variable (‘the effect’). 

Let’s continue with the aforementioned example:

We’ve established that ice cream and swimsuit sales correlate positively in the summer due to temperature rises. In this example, temperature has a causal relationship with both ice cream and swimsuit sales separately. When the temperature rises, more people purchase ice creams; when the temperature rises, more people purchase swimsuits.

The Difference between Correlation and Causation

It’s crucial to recognize that correlation does not imply causation. While correlated variables may exhibit similar patterns, correlation alone does not establish a causal relationship. The key differences between correlation and causation include:

  • Correlation demonstrates the connection between things, while causation reveals if one thing causes another.
  • Correlation is just about the numbers, while causation needs more evidence to prove.

Reflecting on the initial statement: variables with a causal relationship are indeed related, meaning causation always implies correlation. However, as variables can be related without directly influencing each other, correlation does not imply causation

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Correlation does not Imply Causation

Let’s understand a few reasons why we cannot imply causation even if we identify correlation:

  • Third/Confounding Variable Issue: Sometimes, another factor causes the two things we’re studying to be related.
  • This other factor affects both of them individually, so the things we’re studying might not actually cause each other.
  • Directionality Issues: Arise when it’s difficult to determine which variable is the cause and which is the effect. This makes it tricky to say for sure if one thing is causing the other.
  • Chain Reaction: There may be multiple different variables influencing the correlation between the two variables being studied. This chain reaction implies a lack of a cause-and-effect relationship between the variables, instead indicating a correlation. 

In exploring this disparity, it’s crucial to consider concepts like linear relationships and randomized, controlled experimental designs. For instance, conducting controlled experiments to collect data on the sale of ice cream can help elucidate the causal relationship between temperature and ice cream sales.

Testing for Causation in Your Product

When determining if your product causes a particular outcome or behavior, it’s crucial to test it rigorously and precisely.

 Here are some steps you can take to test for causation:

  1. Define Your Hypothesis

Start by clearly defining the hypothesis you want to test. What specific effect do you believe your product has on users? For example, if you’re testing a new fitness app, your hypothesis might be that using the app leads to an increase in daily exercise.

  1. Design experiments

Create controlled experiments to isolate the effect of your product from other variables. This might involve dividing users into different groups, such as a treatment group that uses your product and a control group that does not. Ensure that the groups are similar in all other aspects except for the presence of your product.

  1. Measure Relevant Metrics

Identify key metrics that reflect the desired outcome of your product. These could include changes in user behavior, attitudes, or performance indicators. Use reliable measurement tools to track these metrics accurately over time.

  1. Analyze the data causally.

Use statistical methods and analysis techniques that allow you to draw causal conclusions from your data. Seek evidence that shows a causal relationship between your product’s usage and the observed data in the measured metrics.  Be cautious of confounding variables, and make sure that your product is the clear cause of any observed effects.

  1. Consider Alternative Explanations

Evaluate alternative explanations for the observed results. Could factors other than your product be influencing the outcome? Account for potential confounding variables and ensure they are controlled for in your analysis.

  1. Replicate Findings

Repeat your experiments to validate your results and ensure their reliability. Replication increases confidence in the causal relationship between your product and the observed effects.

By following these steps, you can gain valuable insights into the causal impact of your product on user behavior and outcomes. This information can inform product development, marketing strategies, and decision-making processes, ultimately leading to more effective and successful products.


Comprehending the disparity between correlation and causation is paramount across diverse arenas, encompassing science, economics, and public policy. While correlation unveils associations between variables, causation elucidates the mechanisms propelling those associations. By discriminating between the two, researchers and decision-makers can formulate more precise conclusions and craft informed decisions.

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FAQs on Correlation and Causation

What does ‘correlation does not imply causation’ mean?

A few reasons why correlation may not imply causation are;

  • Directionality issues make it hard to pinpoint which variable is independent and which is dependent. 
  • A chain reaction involving multiple other extraneous variables results in the correlation between the variables being studied.

What is a correlation?

A correlation defines a statistical association between variables.

What is causation?

Causation suggests that altering one variable triggers a change in the other, demonstrating a causal connection between them.

What is the difference between correlation and causation?

It’s crucial to recognize that correlation does not imply causation. While correlated variables may exhibit similar patterns, correlation alone does not establish a causal relationship. The key differences between correlation and causation include:

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