How founders confuse correlation with causation

You changed the button color on Tuesday. Wednesday’s sales were up 15%. The button color worked! Except: a popular blog happened to mention your product on Tuesday night. The traffic spike from that mention drove the sales increase. The button color had nothing to do with it. But without knowing about the blog mention, the attribution to the button seemed obvious. This is the correlation-causation trap, and founders fall into it constantly.

Correlation means two things happen together. Causation means one thing makes the other happen. These are very different, but human brains constantly confuse them. In analytics, this confusion leads to wrong conclusions and wasted effort.

Why the brain conflates correlation and causation

The cognitive shortcuts:

Pattern matching is automatic

The brain evolved to detect relationships between events. See something happen after another thing, assume connection. This heuristic was useful for survival. It’s less useful for analytics.

Temporal sequence suggests causation

If A happens before B, it feels like A might have caused B. “Post hoc ergo propter hoc”—after this, therefore because of this. The logical fallacy is ancient because the cognitive error is innate.

Explanation feels better than mystery

Unexplained outcomes are uncomfortable. When conversion improves, the brain wants to know why. Any available explanation gets grabbed. The discomfort of mystery drives acceptance of false causes.

Control narrative is appealing

“I did X and Y happened” feels empowering. The alternative—“Y happened and I don’t know why”—feels powerless. The desire for control biases toward causal attribution.

How false causation appears in e-commerce

Common manifestations:

Change attribution

“We changed the headline and conversion improved.” But ten other things also changed that week. Seasonal traffic shifted. Competitor ran out of stock. Affiliate published review. The headline change is just the most visible candidate.

Marketing channel credit

“We increased Facebook spend and revenue grew.” But brand awareness was building from months of other activities. Word of mouth was spreading. The Facebook spend correlated with the timing but may not have caused the growth.

Feature impact claims

“We added reviews and sales increased.” Maybe. Or maybe sales were increasing anyway. Or maybe a shipping improvement happened simultaneously. Features get credit for coincidental timing.

Problem attribution

“We changed the checkout and sales dropped.” Or sales dropped for other reasons and the checkout change was coincidental. Attribution works both ways—blame also follows correlation, not just credit.

The alternative explanations founders miss

What else might explain the correlation:

Third variable causing both

Ice cream sales and drowning deaths are correlated. Ice cream doesn’t cause drowning. Summer causes both. In business, seasonality, economic conditions, or market shifts might drive two metrics that seem related.

Reverse causation

“Stores with more staff have higher sales.” Did staff cause sales, or did sales require more staff? The causal arrow might point the opposite direction than assumed.

Selection effects

“Customers who use live chat have higher order values.” Maybe engaged high-intent customers both use chat and spend more. Chat didn’t cause spending; customer type caused both.

Coincidental timing

Things happen at the same time by chance. With enough variables changing constantly, some will correlate by accident. Coincidence looks like causation.

Lagged effects

The actual cause happened weeks ago. Today’s effect gets attributed to this week’s actions. The true cause is separated in time and invisible.

The consequences of false causation

Why this matters:

Wrong strategies get reinforced

If you believe X caused success but it didn’t, you’ll do more X. Resources go to ineffective actions. The real cause remains unknown and underinvested.

Working strategies get abandoned

If you believe X caused failure but it didn’t, you’ll stop X. Effective strategies get dropped because they correlated with bad outcomes they didn’t cause.

Learning is corrupted

Every false causal inference adds wrong information to your mental model of the business. Over time, your understanding diverges from reality. You know things that aren’t true.

Overconfidence builds

“I know what works.” But if causal beliefs are wrong, confidence is false. Overconfidence from false causation leads to larger, more consequential mistakes.

How to test for actual causation

More rigorous approaches:

Controlled experiments

A/B tests where you change one variable and compare to control. If the only difference between groups is the variable and outcomes differ, causation is more plausible. Controls eliminate alternative explanations.

Multiple instances

If you believe X causes Y, does it work multiple times? One instance is weak evidence. Repeated correlation across different contexts strengthens causal inference.

Remove and observe

If you think the new feature drives sales, try removing it. Do sales drop? Reversal tests whether the relationship is actually causal.

Mechanism identification

Can you explain how X would cause Y? A plausible mechanism makes causation more believable. No coherent mechanism is a red flag for false causation.

Timing analysis

Did the effect start precisely when the cause happened? If the timing doesn’t align cleanly, causation is less likely. Precise timing strengthens causal claims.

Building causation skepticism

Mental habits:

The alternative explanation habit

When you think X caused Y, ask: What else might explain Y? Generate at least three alternative explanations before accepting X as cause. Alternatives prevent premature closure.

The coincidence question

Could this be coincidence? With how many things happening daily, what’s the probability that something would correlate with the outcome by chance? Often higher than intuition suggests.

The base rate check

How often does Y happen without X? If conversion improvements happen regularly, this improvement might be routine variance, not caused by your intervention.

The humility stance

“I don’t actually know if X caused Y.” Admit uncertainty. Premature certainty about causation prevents learning. Humility keeps inquiry open.

Language that reveals causal thinking

What to notice in yourself and others:

“We did X and Y happened”

This framing implies causation while only describing correlation. The sentence structure sneaks causal assumption into factual statement.

“X drove Y”

Explicit causal language. Sometimes warranted. Often premature. Notice when you use causal verbs and ask if you’ve actually established causation.

“Because of X, Y”

Asserts cause directly. Sounds confident. Confidence may not be warranted. The strength of the language should match the strength of evidence.

“X works”

Implies X causes good outcomes. Based on what evidence? “Works” is a strong causal claim that needs strong support.

Organizational patterns that worsen the problem

Structural factors:

Success needs a story

“Why did we hit target?” needs an answer. Pressure to explain success creates pressure to attribute causes. Weak explanations get promoted to satisfy organizational need for narrative.

Accountability requires attribution

“Who gets credit?” Rewards require knowing who caused success. This creates incentive to claim causation even when unwarranted.

Learning reviews demand conclusions

“What did we learn from this campaign?” The question assumes learnings exist. Sometimes correlation is all there is, but the review format requires conclusions.

Expertise claims rest on causal knowledge

“I know what works in our business.” Expertise implies causal understanding. This creates incentive to assert causation that may not exist.

Better practices for causal inference

Improving organizational reasoning:

Language standards

“X correlated with Y” rather than “X caused Y” when causation isn’t established. Precise language prevents implied causation from sneaking through.

Required alternatives

Before accepting a causal explanation, require documentation of alternative explanations considered. Make alternative thinking explicit and mandatory.

Replication requirements

Don’t accept causal claims from single instances. Require repeated observation or controlled testing. Single observations suggest hypotheses, not conclusions.

Uncertainty acknowledgment

“We think X might have contributed to Y, but we’re not certain.” Create culture where uncertainty is acceptable. Certainty isn’t always available, and pretending it is causes harm.

Frequently asked questions

How can I ever know what caused something?

Perfect certainty is often unattainable. But you can increase confidence through controlled experiments, repeated observation, mechanism identification, and elimination of alternatives. Practical certainty is achievable even if philosophical certainty isn’t.

Doesn’t correlation still suggest where to look?

Yes. Correlation is useful for generating hypotheses. The problem is stopping at hypothesis and treating it as conclusion. Correlation suggests; experiments confirm.

What about intuition from experience?

Experience builds pattern recognition that can be valuable. But experienced intuition is also subject to the same biases. Experience doesn’t immunize against correlation-causation confusion; it might even strengthen it through repeated false confirmations.

Is it worth the effort to establish causation properly?

For important decisions, yes. For minor optimization, maybe not. The rigor should match the stakes. Small bets don’t need rigorous causal proof. Big strategic shifts do.