Correlation & Causation

Correlation Does Not Equal Causation: Especially in the Age of AI.

This is a fun one. Here's a blog post that explains why correlation does not equal causation, including historical examples to illustrate the point.

In data-driven work, few principles are repeated more often than “correlation does not equal causation.”

And yet, few are violated more frequently.

As datasets grow larger, dashboards more polished, and AI tools more accessible, it has become easier than ever to surface relationships between variables. What hasn’t changed is the difficulty of answering the more important question:

Does one thing actually cause the other — or do they simply move together?

Understanding the difference isn’t academic. It’s foundational to sound research, credible insight, and effective decision-making.

Correlation vs. Causation: A Practical Distinction

Correlation describes a statistical relationship between two variables — they change together in a consistent way.

Causation implies a cause-and-effect relationship — one variable directly influences the other.

Correlation can point you toward interesting questions.

Causation is what allows you to answer them with confidence.

The problem arises when correlation is treated as proof.

Why Correlation So Often Misleads

There are three common reasons correlations get mistaken for causation:

1. Third Variables

An unmeasured factor influences both variables, creating the illusion of a direct link.

Classic example:

Ice cream sales rise in summer. Drowning incidents rise in summer.

Ice cream does not cause drowning — warm weather drives both.

2. Coincidence

Sometimes relationships exist purely by chance, especially in large datasets where patterns are easy to find but hard to validate.

3. Reverse Causality

Even when two variables are linked, the assumed direction of influence may be wrong.

These issues don’t disappear with better tools. In many cases, they become harder to spot.

A Real-World Research Example

Consider a telephone survey designed to capture voter sentiment ahead of a U.S. election. The study runs on a single Monday night, shortly after a major election news cycle.

The topline looks solid — party splits are as expected. But one finding stands out:

• Likely voters appear to watch significantly less football.

At face value, this might suggest that football viewers are less politically engaged.

That conclusion would be wrong.

The survey was conducted during Monday Night Football.

Respondents who were watching didn’t answer the phone.

There was no multi-day fielding, no replicate management, and no correction for availability bias.

The result wasn’t a behavioral insight — it was a methodological artifact.

This is how correlations turn into false narratives.

Where AI Raises the Stakes

AI-powered research tools — including synthetic respondents — introduce new efficiencies and new risks.

Synthetic samples can generate:

• Large volumes of data quickly

• Internally consistent responses

• Highly plausible correlations

What they cannot always generate is true causality grounded in lived human behavior.

Why?

Because synthetic respondents are built on:

• Training data

• Assumptions about how people think and answer

• Pattern replication, not real-world consequence

The result is often clean correlations without underlying mechanisms — relationships that look credible, align with expectations, and reinforce existing beliefs, but lack causal grounding.

Used carefully, AI can accelerate hypothesis generation and scenario testing.

Used uncritically, it can amplify false certainty.

In other words: AI is excellent at simulating correlation.

Causation still requires human-designed rigor.

What It Takes to Establish Causation

Moving from correlation to causation typically requires:

• Thoughtful research design

• Control or comparison groups

• Proper sampling and fielding

• Replication over time

• Triangulation across methods

• Human judgment informed by context

These fundamentals haven’t changed — even as the tools have.

The Bottom Line

Correlation is a starting point, not an answer.

In an era of faster research and AI-generated data, the real differentiator isn’t who can produce insights quickest — it’s who can defend them under scrutiny.

The most valuable research doesn’t just reveal patterns.

It explains why those patterns exist — and what would need to be true for them to change.

That’s the difference between information and insight.