George Lindley

Correlation does not equal causation…

Correlation does not equal causation…

Correlation does not equal causation…

Correlation does not equal causation…

Correlation does not equal causation…

You’ve probably heard this mantra in a statistics class—or seen it scrawled across a whiteboard somewhere. It’s a core principle of data science: just because two variables move together doesn’t mean one caused the other.

The classic example? Ice cream sales and shark attacks both rise in summer. But it’s not because sharks enjoy dessert—there’s a third variable: hot weather.

Everyone accepts this. Statisticians, analysts, even researchers in artificial intelligence.

But here’s the problem:
In business, we act as if correlation does imply causation.
And we do it every day.

This quiet contradiction sits at the heart of how modern companies use data—
and the Causal AI revolution is here to fix it.

Current Practice: How Businesses use Data Today

Let’s start by understanding the typical business data journey—from collection to decision-making.

Data is collected, often passively, through company operations: sales records, support tickets, customer interactions, and increasingly, product usage logs. These form the “data wells” that analysts draw from.

Then comes exploratory analysis. The business analyst examines trends, correlations, and anomalies. They look for patterns that might explain—or at least hint at—business outcomes. For example:

• If a customer complains and isn’t refunded within 7 days, there’s a 95% chance they won’t return.
• If a student stays up to date in weeks 1–6, there’s a 99% chance they’ll finish the course.

These are probabilistic associations, not causal claims. Analysts present them with careful language:

“There is insufficient evidence to confirm a statistically significant relationship…”

In short, the analyst remains a scientist. They stay true to the statistical mantra:

Correlation does not imply causation.

The Contradiction (or the Crime)

But here’s the contradiction—the crime hiding in plain sight.

It’s not the analyst who acts on the data. It’s the decision-maker—the head of operations, the marketing lead, the director. They’re the ones who must decide:

• Should we automate more refunds, based on that customer complaint correlation?
• Should we make Weeks 1–6 easier, hoping to increase course completion rates?

And in making those decisions, they often do what the analyst wouldn’t dare:
They assume causality.

They pull the lever on A hoping B will rise. It’s rarely said out loud, but it’s there—the leap from association to intervention.

Now, we don’t blame the business leader for doing this. In fact, they’re often the best positioned to make these calls. They use intuition, experience, and gut feel—those same instincts that have broken the “correlation ≠ causation” rule a thousand times before.

But let’s be honest:
The analyst chose which correlation to spotlight. They knew exactly how it would be read. They just refused to get their hands dirty.

This isn’t a critique of one company or a few analysts—it’s the standard model of how data analytics is taught, even at the Master’s level. It’s seen as clean, scientific, rigorous.

The analyst wears the white coat (Master of Science).
The manager takes the leap (Master of Arts).

But this tidy division hides a real weakness. Data is being used to imply causation—but without the tools or language to do it responsibly.

The Causal AI Revolution – We CAN Infer Causation

Let’s now bring you up to speed with a little known revolution that is happening across certain domains, soon to be many domains, and it’s all to do with causality. By leaving the decision to the businessman’s intuition, it is of course difficult to teach a computer how to develop the intuition and to judge causality. Very difficult in fact, and this is one way that experts can still catch an AI Model in the Turing Test, by asking questions about causality.

It was long known that causality is a thing. It’s not magic, and do infer causality everyday. In fact if you have kids, you will have noticed even babies ‘getting what they want’. A scream not just correlates, but causes mama to come running. The cylinder block goes through only the cylinder hole. A wave to a stranger is normally returned. It’s only now however causality is being studied – by scholars such as Judea Pearl (winner of the Turing award). A new mathematical language has been developed, as well as programming languages to help ‘teach’ computers to think causally.

So what’s the main breakthrough? It’s not that correlation can imply causation. Again, the above mantra isn’t refuted, but perhaps we can stop singing it so loudly. The breakthrough is to first create a ‘causal model’ – a series of assumptions that we know about the variables. We create a series of assumptions, that is turned into a causal model, and only then do we know what data needs to be collected. Specific data is collected (even created) for us to then feel confident about implying causation!

Two Examples of Causal Thinking in Business

Let’s look at two simple examples where a business might act on correlation—and how causal thinking would completely reshape the analysis and the data strategy.

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1. Fast Refunds = Better Reviews?

A customer service analyst notices a pattern: when customers get their refund quickly, they’re more likely to leave a positive review. Based on this, someone might suggest we automate and speed up refunds. That sounds good—but what if we’re mistaking correlation for causation?

A causal thinker would say: maybe it’s not the speed of the refund causing the good review. Maybe it’s that some types of complaints—say, about late delivery—get refunded quickly and aren’t that serious, so customers feel generous. Or maybe customers who get refunds fast had better delivery experiences in the first place.

We can’t know what causes what unless we model the situation.

Causal model:
Complaint Type and Delivery Experience both influence both the refund speed and the review score.

To actually infer causality, we’d need data on:

• the type of complaint
• the delivery delay
• the product category
• whether the refund was automatic or reviewed
• customer history, like whether they’re a long-time shopper

With this richer dataset and a causal model, we could use tools like DoWhy to simulate:

“If we speed up refunds, will the review score improve for the same customer and complaint type?”

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2. Week 6 Completion = Course Completion?

In an education platform, data shows students who complete the first 6 weeks of a course are very likely to complete the whole thing. Management decides to invest heavily in making weeks 1–6 easier. Again—correlation may be fooling us.

What if the students who make it to week 6 are just the most motivated or best prepared from the start? Maybe they were going to finish anyway, no matter how hard the course was.

Causal model:
Motivation and Prior Knowledge both influence whether someone gets past week 6 and whether they finish the course.

To even begin estimating a causal effect, we’d need to measure:

• prior ability (via GPA or a placement score)
• effort (study hours logged, login frequency)
• external constraints (job hours, family status)
• module difficulty for Weeks 1–6

With that, we could ask:

“If all students completed Week 6 (even the low-motivation ones), would we actually see an increase in course completion?”

Only a causal model can help answer that.

The Future of Business Decision Making

In both examples—the refund policy and the course design—the naive move might work, or it might backfire. The correlations are tempting, but without understanding the underlying system, we risk making expensive mistakes.

If we don’t ask the right causal questions and collect the right data, we’re building business decisions on sand.

Causal AI gives us the language and tools to think in terms of interventions, not just associations. That’s what sets it apart from traditional analytics—and why it’s going to reshape how businesses reason, test, and act.

Business has always run on causality. Now, finally, analytics can catch up!