The bigger the dataset, the more likely we are to mistake correlation for causation. The good news is that causal thinking can be learned.

Everyone knows that correlation does not imply causation. And proper causal thinking is a concept at the heart of any good business decision making.

Yet a recent paper highlights what it calls the big data fallacy: the bigger the dataset, the more likely decision-makers are to mistake correlation for causation. That leads to bad decisions, because no amount of data can do this job without a causal model.

It's the same instinct behind a question I get a lot from data scientists. After a talk on causal modelling, someone will ask: "Won't a predictive model be able to solve this if it just has more data?"

No, it won't.

(See The simple task your AI can't learn.)

So what did the paper find?

Across twelve preregistered experiments with 6,408 participants, they found the same pattern again and again:

• Show people a correlational finding from a small observational study, and they treat it with appropriate caution.
• Show them the same finding from a massive dataset, and suddenly they act on it as if it were causal.

This finding held even when participants were shown a well-powered randomised experiment showing no effect at all, or even the opposite effect.

Although the paper doesn't study this, I suspect that the same would be found if they studied model complexity. The bigger and more impressive the model sounds (a deep neural net rather than a humble regression, say), the more likely people are to wrongly treat it as causal.

Experienced executives weren't immune

The uncomfortable part is who fell for it. In one experiment, 44 executives with an average of 19 years' business experience showed a stronger big data fallacy than a general online sample. Most of them answered a basic causal reasoning question correctly, and still made the wrong call. The very people making the decisions were no better protected.

And it survived every quick fix the authors tried: reciting the "correlation is not causation" mantra, prompting people to list confounding variables (which, I'm convinced, most people don't properly understand, but that's for another post). Awareness alone simply didn't move the needle.

A biased estimate from 200 million rows is still biased. It's just a very precise wrong answer, delivered with high confidence.

Why does this happen?

The paper shows that people substitute an easy question (which dataset is bigger and better?) for the harder but correct one: do this data and this model actually support a causal conclusion? Large samples genuinely give you precision (higher certainty) and representativeness. But they do nothing about confounding, bad controls, reverse causality, or selection effects.

What to do about it

If one-off reminders and awareness campaigns don't fix the problem, what does? Causal thinking. Scientific thinking. A causal mindset. You need methods that force the causal question to be asked explicitly, and people who instinctively ask it.

Big data is a wonderful thing. But volume answers "how certain?" and "how representative?", but not "why?" If your organisation is making decisions on best actions from correlational dashboards or predictive models, however large the datasets, it's worth a rethink.