from Hacker News

The Illusion of Causality in Charts

by skadamat on 5/28/25, 5:49 PM with 34 comments

  • by gcanyon on 5/31/25, 2:06 PM

    The article seems more about the underlying causality, and less about the charts' specific role in misleading. To pick one example, the scatterplot chart isn't misleading: it's just a humble chart doing exactly what it's supposed to do: present some data in a way that makes clear the relationship (not necessarily causality!) between saturated fat consumption and heart disease.

    The underlying issue (which the article discusses to some extent) is how confounding factors can make the data misleading/allow the data to be misinterpreted.

    To discuss "The Illusion of Causality in Charts" I'd want to consider how one chart type vs. another is more susceptible to misinterpretation/more misleading than another. I don't know if that's actually true -- I haven't worked up some examples to check -- but that's what I was hoping for here.

  • by nwlotz on 5/31/25, 3:59 PM

    One of the best things I was forced to do in high school was read "How to Lie with Statistics" by Darrell Huff. The book's a bit dated and oversimplified in parts, but it gave me a healthy skepticism that served me well in college and beyond.

    I think the issues described in this piece, and by other comments, are going to get much worse with the (dis)information overload AI can provide. "Hey AI, plot thing I don't like A with bad outcome B, and scale the axes so they look heavily correlated". Then it's picked up on social media, a clout-chasing public official sees it, and now it's used to make policy.

  • by djoldman on 5/31/25, 6:53 PM

    This is not a problem with charts, it is a problem with the interpretation of charts.

    1. In general, humans are not trained to be skeptical of data visualizations.

    2. Humans are hard-wired to find and act on patterns, illusory or not, at great expense.

    Incidentally, I've found that avoiding the words "causes," "causality," and "causation" is almost always the right path or at the least should be the rule as opposed to the exception. In my experience, they rarely clarify and are almost always overreach.

  • by justonceokay on 5/31/25, 3:12 PM

    A pet issue I have that is in line with the “illusions” in the article is what I might call the “bound by statistics” fallacy.

    The shape of it is that there is a statistic about population and then that statistic is used to describe a member of that population. For example, a news story that starts with “70% of restaurants fail in their first year, so it’s surprising that new restaurant Pete’s Pizza is opening their third location!”

    But it’s only surprising if you know absolutely nothing about Pete and his business. Pete’s a smart guy. He’s running without debt and has community and government ties. His aunt ran a pizza business and gave him her recipes.

    In a Bayesian way of thinking, the newscasters statement only makes sense if the only prior they have is the average success rate of restaurants. But that is an admittance that they know nothing about the actual specifics of the current situation, or the person they are talking about. Additionally there is zero causal relationship between group statistics and individual outcomes, the causal relationship goes the other way. Pete’s success will slightly change that 70% metric, but the 70% metric never bound Pete to be “likely to fail”.

    Other places I see the “bound by statistics” problem is in healthcare, criminal proceedings, racist rhetoric, and identity politics.

  • by NoTranslationL on 5/31/25, 1:40 PM

    This is a tough problem. I’m working on an app called Reflect [1] that lets you analyze your life’s data and the temptation to draw conclusions from charts and correlations is strong. We added an experiments feature that will let you form hypotheses and it will even flag confounding variables if you track other metrics during your experiments. Still trying to make it even better to avoid drawing false conclusions.

    [1] https://apps.apple.com/us/app/reflect-track-anything/id64638...

  • by singularity2001 on 5/31/25, 10:18 PM

    what's very fascinating in general is that causality is a difficult mathematical concept which only a tiny fraction of the population learns yet everyone is talking about it and "using it"

    we do have a pretty good intuition for it but if you look at the details and ask people what is the difference between correlation and causality and how do you distinguish it things get rabbit holey pretty quick

  • by JackSlateur on 6/1/25, 10:04 PM

  • by qixv on 5/31/25, 7:53 PM

    You know, everyone that confuses correlation with causation ends up dying.