One thing that everyone on the internet knows, about statistics, is this: correlation does not imply causation. It’s a stock phrase, a bauble constantly polished and passed off in internet debate. And it’s not wrong, at least not on its face. But I worry that the denial of the importance of correlation is a bigger impediment to human knowledge and understanding than belief in specious relationships between correlation and causation.
First, you should read two pieces on the “correlation does not imply causation” phenomenon, which has gone from a somewhat arcane notion common to research methods classes to a full-fledged meme. This piece by Greg Laden is absolute required reading on correlation and causation and how to think about both. Second, this piece by Daniel Engber does good work talking about how “correlation does not imply causation” became an overused and unhelpful piece of internet lingo.
As Laden points out, the question is really this: what does “imply” mean? The people who employ “correlation does not imply causation” as a kind of argumentative trump card are typically using “imply” in a way that nobody actually means, which is as synonymous with “prove.” That’s pretty far from what we usually mean by “implies”! In fact, using the typical meaning of implication, correlation sometimes implies causation, in the sense that it provides evidence for a causal relationship. In careful, rigorously conducted research, a strong correlation can offer some evidence of causation, if that correlation is embedded in a theoretical argument for how that causative relationship works. If nothing else, correlation is often the first stage in identifying relationships of interest that we might then investigate in more rigorous ways, if we can.
A few things I’d like people to think about.
There are specific reasons that an assertion of causation from correlation data might be incorrect. There is a vast literature of research methodology, across just about every research field you can imagine. Correlation-causation fallacies have been investigated and understood for a long time. Among the potential dangers is the confounding variable, where an unknown variable is driving the change in two other variables, making them appear to influence one another. This gives us the famous drownings-and-ice cream correlation – as drownings go up, so do ice cream sales. The confounding variable, of course, is temperature.1 There are all sorts of nasty little interpretation problems in the literature. These dangers are real. But in order to have understanding, we have to actually investigate why a particular relationship is spurious. Just saying “correlation does not imply causation” doesn’t do anything to actually improve our understanding. Explore why, if you want to be useful. Use the phrase as the beginning of a conversation, not a talisman.
Correlation evidence can be essential when it is difficult or impossible to investigate a causative mechanism. Cigarette smoking causes cancer. We know that. We know it because of many, many rigorous and careful studies have established that connection. It might surprise you to know that the large majority of our evidence demonstrating that relationship comes from correlation studies, rather than experiments. Why? Well, as my statistics instructor used to say – here, let’s prove cigarette smoking causes cancer. We’ll round up some infants, and we’ll divide them into experimental and control groups, and we’ll expose the experimental group to tobacco smoke, and in a few years, we’ll have proven a causal relationship. Sound like a good idea to you? Me neither. We knew that cigarettes were contributing to lung cancer long before we identified what was actually happening in the human body, and we have correlational studies to thank for that. Blinded randomized controlled experimental studies are the gold standard, but they are rare precisely because they are hard, sometimes impossible. To refuse to take anything else as meaningful evidence is nihilism, not skepticism.
Sometimes what we care about is association. Consider relationships which we believe to be strong but in which we are unlikely to ever identify a specific causal mechanism. I have on my desk a raft of research showing a strong correlation between parental income and student performance on various educational metrics. It’s a relationship we find in a variety of locations, across a variety of ages, and through a variety of different research contexts. This is important research, it has stakes; it helps us to understand the power of structural advantage and contributes to political critique of our supposedly meritocratic social systems.
Suppose I was prohibited from asserting that this correlation proved anything because I couldn’t prove causation. My question is this: how could I find a specific causal mechanism? The relationship is likely very complex, and in some cases, not subject to external observation by researchers at all. To refuse to consider this relationship in our knowledge making or our policy decisions because of an overly skeptical attitude towards correlational data would be profoundly misguided. Of course there’s limitations and restrictions we need to keep in mind – the relationship is consistent but not universal, its effect is different for different parts of the income scale, it varies with a variety of factors. It’s not a complete or simple story. But I’m still perfectly willing to say that poverty is associated with poor educational performance. That’s the only reasonable conclusion from the data. That association matters, even if we can’t find a specific causal mechanism.
Correlation is a statistical relationship. Causation is a judgement call. I frequently find that people seem to believe that there is some sort of mathematical proof of causation that a high correlation does not merit, some number that can be spit out by statistical packages that says “here’s causation.” But causation is always a matter of the informed judgment of the research community. Controlled experiments are the gold standard in that regard, but there are controlled experiments that can’t prove causation and other research methods that have established causation to the satisfaction of most members of a discipline.
Human beings have the benefit of human reasoning. One of my frustrations with the “correlation does not imply causation” line is that it’s often deployed in instances where no one is asserting that we’ve adequately proved causation. I sometimes feel as though people are trying to protect us from mistakes of reasoning that no one would actually fall victim to. In an (overall excellent) piece for the Times, Gary Marcus and Ernest Davis write, “A big data analysis might reveal, for instance, that from 2006 to 2011 the United States murder rate was well correlated with the market share of Internet Explorer: Both went down sharply. But it’s hard to imagine there is any causal relationship between the two.” That’s true – it is hard to imagine! So hard to imagine that I don’t think anyone would have that problem. I get the point that it’s a deliberately exaggerated example, and I also fully recognize that there are some correlation-causation assumptions that are tempting but wrong. But I think that, when people state the dangers of drawing specious relationships, they sometimes act as if we’re all dummies. No one will look at these correlations and think they’re describing real causal relationships because no one is that senseless. So why are we so afraid of that potential bad reasoning?
Those disagreeing with conclusions drawn from correlational data have a burden of proof too. This is the thing, for me, more than anything. It’s fine to dispute a suggestion of causation drawn from correlation data. Just recognize that you have to actually make the case. Different people can have responsible, reasonable disagreements about statistical inferences. Both sides have to present evidence and make a rational argument drawn from theory. “Correlation does not imply causation” is the beginning of discussion, not the end.
I consider myself on the skeptical side when it comes to Big Data, at least in certain applications. As someone who is frequently frustrated by hype and woowoo, I’m firmly in the camp that says we need skepticism ingrained in how we think and write about statistical inquiry. I personally do think that many of the claims about Big Data applications are overblown, and I also think that the notion that we’ll ever be post-theory or purely empirical are dangerously misguided. But there’s no need to throw the baby out with the bathwater. While we should maintain a healthy criticism of them, new ventures dedicated to researched, data-driven writing should be greeted as a welcome development. What we need, I think, is to contribute to a communal understanding of research methods and statistics, including healthy skepticism, and there’s reason for optimism in that regard. Reasonable skepticism, not unthinking rejection; a critical utilization, not a thoughtless embrace.