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Causal ELI5: Correlation vs Causal Models

TL;DR

Traditional AI thinks umbrellas cause rain. Causal AI understands the world. Which one do you think is best?

Imagine you see people carrying umbrellas, and it often rains on those days.
Any human understands perfectly that umbrellas don't cause rain, while traditional AI might think umbrellas cause rain, even if it is not true. This is the difference between correlation and causation.

  • Correlation means identifying how variables are related.

    • Example: Ice cream sales go up when it is hot. They move together, but one does not cause the other.

  • Causality means finding out if one variable actually causes another.

    • Example: Turning on the air conditioner causes the room to cool down.

Both correlation-based and causation-based AI models:

  • Take inputs and make predictions
  • Find relationships between variables

But they start from different places.

Correlation-Based AI: "Data First"#

Correlation-based AI starts with data.
It collects as much data as possible and looks for patterns.
It works best when there is plenty of historical and observational data.

The Process#

  1. Acquire data
  2. Integrate and clean data
  3. Explore the data (EDA)
  4. Engineer features
  5. Build and test models
  6. Deploy models into production

Why Many Correlation-Based AI Projects Fail#

Even with large amounts of data, many projects struggle because:

  • Organizational or cultural barriers
  • Models that are black boxes and hard to explain
  • Spurious correlations (false patterns that look real)
  • No clear goal or purpose for doing machine learning

Without understanding why something happens, correlation-based AI can make confident but incorrect predictions.

Causation-Based AI: "Model First"#

Causal AI begins with the problem, not the data.
It asks, "What are we trying to achieve?" before any data is collected.

The Process#

  1. Define the intended outcome
  2. Propose an intervention (what action might influence the result)
  3. Identify confounding factors (things that might distort the result)
  4. Identify effecting factors (things that directly change the result)
  5. Create a causal model or diagram
  6. Acquire and use data to test and refine the model

Causal AI is like running an experiment.
It aims to understand the mechanism behind what happens, not just the pattern.

Summary Table#

Aspect Correlation-Based AI Causation-Based AI
Starting point Data first Model first
Focus Relationships Cause and effect
Best for Historical data Business questions
Main risk False patterns, unclear goals Needs expert knowledge

Correlation tells you what happens together.
Causation tells you why it happens.

Both are valuable, but if you want AI that makes smart and reliable decisions,
you need to understand not just the connection but the cause.