As is the case with all of the Falsehoods in the Falsehoods series, one can never really be sure what the falsehood may actually be. In this case, there are two falsehoods: 1) When we see a statistical correlation between two measurements or observations, we can not assume that there is a causal link from one to the other. This is the way the statement “Correlation does not imply causality” or some similar version of that aphorism generally means, and this is an admonishment we often hear; and 2) When we see a statistical correlation between two measurements or observations, there probably is a causal link in there somewhere, even when we hear the admonishment “Correlation does not imply causality” from someone, usually on the Internet. To put a finer point on this: What do you think people mean when they say “Correlation does not equal causality?” or, perhaps more importantly, what do you think that statement invokes in other people’s minds?

When I hear it I usually think “Don’t be a dumbass.” I mean, really, nobody is thinking that a mere statistical correlation means that two sets of observations have a definitive causal link. Almost always a correlation is being referred to because there is reason to suspect a causal link between two things, and this link is, we suspect, illustrated by this correlation.

When we hear “Causation doesn’t imply causation” is the person saying that two series of, say, 200 pairs of numbers that closely describe a straight line or a nice well behaved curve on a graph are not so seemingly linked because of causation happening somewhere, and that its just random? Often, yes, that is what they are saying. Recently, a friend of mine mentioned a possible link between a number of physical things about herself and a described medical syndrome, and a friend of hers said “That’s correlation. Correlation doesn’t mean causation.” I thought that was an interesting example of the use of the phrase. My friend with the interesting symptoms was not comparing a series of measurements of two phenomena, but rather, a series of attributes, and a mixture of quantitative and qualitative attributes at that, and how well they matched a similar list thought to be linked to a certain condition. She was diagnosing, not measuring. She was carrying out a Peircian abductive inference, not a quantitative induction. Yet the phrase came up in a rather scolding manner, from a well meaning yet somehow paternalistic observer. And it meant, as it often does, nothing helpful.

To explore this concept further, let’s examine what we think “causality” is at a basic level: Most of the time, when we use some variant of that term, we mean that one thing is causing another thing. Gravity causes the apple to fall to the ground instead of sideways when it detaches from the tree (although we are saying nothing about why it un-attached from the tree, so we are not giving a full causal explanation for the observation). Pacific El Nino cycles cause corresponding cycles of aridity or increased rainfall in other parts of the world. Heavy traffic causes my drive to be longer. And so on.

Sometimes, we have reason to believe that two things co-vary because of one or more external causes. Aridity in one region of the world is correlated with higher rainfall in another reason of the world, and it turns out that both meteorological variations are caused by the effects of the Pacific El Nino. Quite often, especially in complex systems like are often dealt with in the social sciences, we can replicate correlations among various phenomena but we may have multiple ideas about what the causal structure underlying the phenomenon at hand may be. Repeated observations rule out random associations or meaninglessness in the data, but we are faced with multiple alternative models for where to put the causal arrows. In other words, we’re pretty sure there is a “causal link” somewhere, but we can’t see, or agree amongst ourselves, on what it is.

For instance, there is an association between hunting success (by males) in some forager groups and what might be called “mating success” measured as either married/not married; age of first marriage; married for more years vs. fewer; one vs. more than one wife; fewer vs. more children. (There have been a number of studies using a number of variables.) I’m pretty sure that there are two distinctly different causes for this “correlation.” 1) Better hunters are preferred by some women; and 2) Men who are married and, especially, have a couple of kids, are compelled to be more successful at hunting. (The truth is that most forager men are excellent hunters; Day to day variation in success is mostly random; Therefore hunting “success” can be most reliably increased by hunting more and possibly by simply focusing on the effort more keenly rather than screwing off.) Both causes are probably at work in most systems. The causal arrows are much more varied and fickle than the very arrows the men carry in their quivers.

This means that if you find a correlation between some measure of hunting success and some measure of mating success in a group of Hunter-Gatherers, the statement “correlation does not imply causation” is meaningless, though the statement “the specific model you present to explain your data is wrong in that you have causation backwards” may be correct! Or not!

Scientists (and others) often arrive at a point where they assume, pragmatically, that there is a causal link between two things even when the link can’t be explained in a coherent model. In fact, this happens quite often and is probably what directs a lot of research, as novel experiments or exploratory programs are designed to pin down such a model. When this happens, the presumption of causality has been derived from mere correlation. It has been said (go look it up in Wikipedia) that correlation does not prove causation, but it can be a hint. In practice, and logically, there is too large a gap between the statement “Correlation implies or proves causality” and “Correlation is a hint.” Correlation is as good as the data, its replicatbility, the relevant statistics, and its p-values. If you take numerous honest stabs at a relationship between phenomena, measure things a few different ways to help rule out a bias in how that is being done, avoid doing stupid statistics (like accidentally correlating a variable to itself), replicate with the same results, don’t throw out trials unless there is a valid reason to do so, the statistics are sufficiently robust or at least correctly chosen, and the p-values are kick-ass, then your correlations are not hints. No. Your correlations imply causation. They may not imply a simple causal effect with one thing you’ve measured causing change in the other … see the discussion above for where the causal arrows may be pointing. There may be parts of your model missing or obscured, but correlation implies causation.

So, there are several aspect to this fallacy.

“Correlation equals causation” is a misstatement because there are reasons other than causation that correlations between data series can emerge.

“Correlation equals causation” can be wrong because it specifies a causal structure that happens to be wrong, and more subtly but also more importantly, correlation of an “X” variable (on the horizontal axix) and a “Y” variable (on the vertical axis) usually implies, even though this is entirely arbitrary, that X causes Y (X being the independent variable, and Y being the dependent variable). Similarly, an equation “Y = mX + b” seems to be saying that X causes Y. Similarly, a statement like “when we increase altitude, temperature seems to decrease” implies that temperature varies as a function of … because of … as something caused by …. altitude. But the fact that things can be ordered this way on a graph, put in this kind of equation, or described with this kind of language does not in and of itself mean that the causal arrow has been spotted and tamed.

“Correlation does not imply causation” is entirely wrong. If, that is, you think the word “imply” means “suggest.” Correlation does indeed “suggest” causation, though it may not suggest a particular directionality or structure of causation. So, if a person says:

“I have a correlation over here. This suggests some kinda causal tingie going on here.”

Then the response:

“No, dear, correlation does not imply causation”

is a dumb-ass thing do say. If, on the other hand, a person says:

“I’ve noticed this correlation between thing one and thing two. This strongly implies an underlying truth consisting of thing one causing thing two”

Then the response:

“OK, that’s interesting, but correlation does not mean causation:”

is a worthy missive.

Finally, and to the reason I wrote this post to begin with. I think there is a correlation between when someone says “correlation does not imply causation” and the person saying that having an agenda other than spreading the word on introductory level statistics. Sometimes it is just an effort to get the person off the topic. In the example above, about the illness, the person was trying to get the affected individual to not link symptoms with some awful diseases as a matter of denial: Hopefulness that the person didn’t really have the disease. In other cases it is more paternalistic. But then there are those instances that are more troubling and possibly more common: Denialism. We see statements like “correlation does not imply causation” when decades of data from multiple sources analyzed a variety of different ways consistently and repeatedly link the release of fossil carbon into the atmosphere with warming, for example. In these cases not only is the statement being used incorrectly and even nefariously, it is being used in a more bizarro-land sort of way: Correlation means that THERE IS NO CAUSATION. How do you get from a strong statistical argument for something to the idea that a strong statistical argument means the opposite of what it means? By having a statement like “correlation does not imply causation” reach aphorism level of inanity. Under such linguistic conditions, statements like “I could not possibly care less than I do about this, meaning that I care not at all” transform to statements like “I could care less” which means the opposite, in words, but the same, in spirit. “Correlation does not bla bla bla” in the denialist context means “Statistics are wrong.” And that’s just wrong.

So, there, I said it but you may not have heard it: “Correlation does not mean causation” or some variant thereof is, sometimes, a dog whistle.

Comments

  1. #1 Kevin Emerson
    June 20, 2011

    Perfect comic for ‘correlation is not causation’

    http://xkcd.com/552/

  2. #2 JLA
    June 20, 2011

    About warming, sure. Most health reporters should get the phrase tattooed on the backs of their hands, so they can see it the next time they start to write a story about how this or that prevents cancer.

  3. #3 lylebot
    June 20, 2011

    I think there’s a certain type of moderately smart person that learns they can parrot phrases like this for some kind of gain without truly understanding what they mean. Maybe it’s because of encouragement from peers or a general feeling of intellectual superiority. I don’t know. I’m sure I’m guilty of the same behavior in other cases though :)

    I wonder, though, when “implies” became part of it. I had always heard it as “correlation is not causation” until relatively recently. “Correlation does not imply causation” is clearly false in general, while “correlation is not causation” is clearly true in general (but not always useful as you point out).

  4. #4 sailor
    June 20, 2011

    Correlation can suggest causation. Sometimes it has nothing to do with causation and is just coincidence. But one thing is for sure, correlation by itself does not prove causation.

  5. #5 Eric Lund
    June 20, 2011

    When somebody reports that A and B are correlated, there are four possibilities:
    1. A causes B.
    2. B causes A.
    3. A and B are not causally related to each other, but some other factor C causes both A and B.
    4. A and B are not causally related to each other, and the apparent correlation is a statistical fluke.

    You can check the last possibility by repeating the test on a larger sample and seeing whether the correlation holds up (you can make the probability of a statistical fluke ridiculously small, although you can never make it go to zero). Proving any of the other possibilities cannot be done with statistics alone. For that, you have to have some kind of model which determines which of the first three categories you are in.

    For instance, if hail is falling at your location, there is almost certainly lightning in the vicinity. But this is not because the hail causes the lightning, or vice versa; it is because a severe thunderstorm, which causes both of these things, is passing through. Thus it is correct to say that the correlation of hail and lightning does not imply that one causes the other. It is, however, a strong indication that the phenomena are related.

  6. #6 John Prof
    June 20, 2011

    I disagree. This is largely semantics. You want “equals” instead of “imply” – fine. Equals is much stronger. But you premise is still wrong. Imply = strongly suggests, and correlation doesn’t strongly suggest. Just because correlation can equal causation, doesn’t mean it suggests it in any way. You are falling into the trap yourself. At best it introduces the possibility.

    Every year, on my birthday, the stock market goes up. My birthday doesn’t cause the stock market to go up (equals).

    Nor does it imply that that my birthday causes the stock market to go up. You can have the strongest correlation possible, and it will have nothing to do with causation.

  7. #7 Greg Laden
    June 20, 2011

    “Imply = strongly suggests, and correlation doesn’t strongly suggest.”

    Imply in formal logic and probably in science does indeed equal strongly suggest. In English, however, it might mean strongly suggest and increasingly means to state something knowing it is not true, or some other weaker version of what it could mean. I do rather prefer to talk about the meaning of a falsehood (and its false-ness) in the context in which it is actually used. But yes, the most common wikipedia version of the phrase is “correlation does not imply causation” and the word “imply” means to indicate a truth (strongly) indirectly.

    So no, my premise is not incorrect … it is correct … and I’ve not fallen into a trap. I’m speaking here of pedantry. Glad you showed up!

    Every year, on my birthday, the stock market goes up. My birthday doesn’t cause the stock market to go up (equals).

    Now, you are condescending. Unnecessary, though if it gives you pleasure ….

    you can have the strongest correlation possible, and it will have nothing to do with causation.

    And THAT is patent nonsense, isn’t it? The stock market goes up all the time. Every year one has a birthday. They are correlated. There is a causal, though trivial, connection: Time goes in one direction.

    Also, your stock market/birthday example is a poorly constructed one; Apples and Orange and all that. Come back when you’ve got an apples to apples example and multiple repeated strong correlations with good p-values and tell me about how there is no causal link of some kind.

    Trap indeed. Indeed.

    Eric, yeah, I’m going to add something like that into the next version of this post.

  8. #8 Greg Laden
    June 20, 2011

    Oh, and John, yes, it is largely semantics. You totally got that part.

  9. #9 Allen MacNeill
    June 20, 2011

    Two variable might show a very strong correlation because they are linked to another variable. A classical example is the correlation between flush toilets and heart attacks in rural _____ (name your developing country). Do flush toilets cause heart attacks? No. Do heart attacks cause flush toilets? No. However, both are also correlated with affluence and especially the diet and sedentary lifestyle that goes with it. So what is the causative factor here? Is it affluence, or diet, or lack of exercise, or economic development, or something else (perhaps a virus that causes arterial lesions that is more likely spread in denser urban populations, perhaps via municipal water systems contaminated with sewage…from flush toilets. To me, safest thing to say about correlation is that “correlation implies correlation” and leave it at that. Causation is much more reliably verified/falsified by controlled experiments, either intentional or accidental, in which one can compare the effects of manipulating a variable with the effects of NOT manipulating that same variable.

  10. #10 DataPacRat
    June 20, 2011

    Correlation doesn’t imply causation, but it does waggle its eyebrows suggestively and gesture furtively while mouthing ‘look over there’.
    — Randall Munroe

  11. #11 D. C. Sessions
    June 20, 2011

    I mean, really, nobody is thinking that a mere statistical correlation means that two sets of observations have a definitive causal link.

    I’m glad that I’ve just been imagining the antivaccine crowd all these years.

  12. #12 Mike McRae
    June 20, 2011

    The big problem is that we can’t observe causes. The term ’cause’ describes a relationship, not a stimulus. Yet I’ve found a lot of people have a hard time with this concept (including, to be honest, even myself).

    Our brains do better assuming that high correlations are synonymous with causations, taking a shortcut to conclude with confidence that a particular observation is the same as a cause until this relationship fails and a better one comes along. It’s a pretty useless thing to describe all events as merely series of unconnected occurrences, after all.

  13. #13 Greg Laden
    June 20, 2011

    Mike, good point. One way to help with this is to square the r to get R-squared, and pretend like that’s a percentage. If your model is good, saying “10 percent of the variation in X is caused by variation in Y2 (for example) has more meaning than saying that “there’s a high/low correlation.”

    So, when the apple falls from the tree to the ground, what is the cause? Gravity? Or whatever effect detached the apple from its branch?

  14. #14 badrescher
    June 21, 2011

    Although I completely understand your frustration with the misuse of this ‘rule’, this post leaves me scratching my head for two reasons and the comments just make me want to bang my head on my desk. Not because they are outright wrong, but because most are so convoluted (one of the things that made me scratch my head about the post) that I can’t imagine the audience you were hoping to reach actually reading it.

    So, first, maybe due to frustration, your attempt at explaining the frustrating problem was convoluted and many statements in it (e.g., “Your correlations imply causation.”) would be simply wrong if taken out of context. I think that you’re trying to explain causal inference at a depth that requires a lot more than a blog post to understand. Mike’s points are well-taken in this regard.

    But you could explain the oversimplification problem that frustrated you to begin with much more simply than you have here and in a way that is much more defendable.

    Logically, correlation is not sufficient evidence to infer cause, but it is necessary to infer cause.

    In other words, all causal relationships are correlational; not all correlational relationships are causal. In other words, correlation alone does not suggest, imply, prove, or equal cause, but without it, we have no evidence that a causal relationship exists.

    I hope that is as clear as I think it is. If it is not, I’d like to know so that I can work on it.

    But then there’s the other thing that bothered me about the post:

    I mean, really, nobody is thinking that a mere statistical correlation means that two sets of observations have a definitive causal link.

    Are you serious? I’m thinking that you must have few friends or family with IQs below 120 if your experience tells you this. Mine, and the research on reasoning, says otherwise. Most people don’t recognize that they have made the assumption, true, and many people misunderstand the ‘saying’ as you noted, but assuming cause from correlational information is one of the most common forms of overgeneralization that people make.

  15. #15 Greg Laden
    June 21, 2011

    I think that you’re trying to explain causal inference at a depth that requires a lot more than a blog post to understand.

    I am definitely NOT trying to explain causal inference at any level at all.

    In other words, all causal relationships are correlational;

    Not necessarily. Chaotic and emerita systems may not be, for instance. Anyway, your description of how causality and correlation may work is fine, but it really does not speak to the post. I think your frustration with my frustration is that you were looking for, or wanting, something that I never put there.

    I can summarize this post very simply: When you hear people say “correlation is not causation” (in one form or another) it is often a denialist ruse, paternalism, or some other sort of distraction from the issue at hand, and the person is generally speaking out of their nether regions.

    I could have said that sentence instead of writing the entire post, but there are sometimes reasons to expand on a point.

    Are you serious? I’m thinking that you must have few friends or family with IQs below 120 if your experience tells you this.

    Again, I think you are looking for something more than intended. Let me rephrase: A statement about a causal link between things made in reference to a statistical observation is not a statement about the statistical observation in the absence of any context that may have led to the discussion, or the conclusion. I think maybe you missed the word “mere.”

    Thanks for your comments, though! As I noted in a comment, I had originally intended to have a part of this post address correlation and causality rather than the aphorism and its uses and meaning, but I didn’t. And, with your comment and others there is a good starting point for that discussion.

  16. #16 badrescher
    June 21, 2011

    Not necessarily. Chaotic and emerita systems may not be, for instance.

    When variables are not isolated, correlations may appear to be nonexistent, but causes are always correlated with effects – by definition. This is what makes the topic so difficult to discuss. The caveats pile up in practical use.

  17. #17 Greg Laden
    June 21, 2011

    Did I say “emerita?” Damn autocorrect. I wonder how many people went to wikipedia to figure that out..

  18. #18 P Smith
    June 21, 2011

    “Correlation does not mean causality”?

    “Correlation does not imply causality”?

    “Correlation does not infer causality”?

    “Correlation does not prove causality”?

    “Correlation does not lead to casualties”?

    I prefer my own:

    Correlation may suggest causality and that further investigation should be done.c

    Unfortunately, it’s not as succinct or as pithy.

    .

  19. #19 M
    June 22, 2011

    Greg,

    I deal with this all of the time in my work as well. I work in researching curriculum and instruction. Often we try to look at some novel instruction in a classroom and look at student understanding of the concepts in the instruction.
    When we deal with those “Correlation does not imply causation” folks, it is usually someone with either an alternative instructional method that has not panned out or it is an administrator looking for an an out to say we can’t afford to teach the students science in this manner. Agenda is often the bane of anyone doing research on living systems.

  20. #20 Courtney
    July 15, 2011

    I generally say, “Correlation does not equal causation.”

    I agree, as you point out, that seeing a correlation in your data automatically makes you think you have found a cause. A scientist will think, “Hmm, perhaps this correlation is due to causation, let me prove/disprove that with another experiment.”

    However, the general public (thank you news media) automatically jumps to correlation equals causation. Therein lies the rub.

  21. #21 Mtop
    July 31, 2011

    I mean, really, nobody is thinking that a mere statistical correlation means that two sets of observations have a definitive causal link.

    Maybe not in your statistically literate circles, but that claim is made frequently in every day life. Often based on the flimsiest of correlations.

    Logically, correlation is not sufficient evidence to infer cause, but it is necessary to infer cause.

    Correlation does not prove causation, but lack of correlation does prove lack of causation.

  22. #22 Frank
    March 8, 2012

    Francis Bacon said, “The general root of superstition is that men observe when things hit, and not when they miss; and commit to memory the one, and forget and pass over the other.” I say coincidence does not prove causation. That works for me in my simple world.

  23. #23 Dan
    May 12, 2012

    [i]And THAT is patent nonsense, isn’t it? The stock market goes up all the time. Every year one has a birthday. They are correlated. There is a causal, though trivial, connection: Time goes in one direction.[/i]

    No. Whenever we say that there is a causation, that causation is between clearly defined things that we’re observing.
    If we say that there’s a causation between a person’s birthday and a stock market movement, the causation must be between those two things, that is someone’s birthday is CAUSING the market to move in a certain direction. Without that person’s birthday at that time, the market wouldn’t have moved that way.
    We can’t make random associations and suggest that there is a causation – that’s a logical fallacy!

    Yeah, some people, especially from social “sciences” and from a few more scientific fields but where real world experimentation is limited or impossible (eg climatology, paleontology, archeology) , will always be extremely uncomfortable with this indisputable reality that correlation does not IMPLY causation, and that correlation by itself or any kind of statistical data NEVER proves anything.

  24. #24 ted
    July 20, 2012

    This is hogwash. Correlation alone does not even imply causation. To imply or to suggest X is to say it is true without saying it (i.e. to imply it is true). Since there is no relationship between just correlation and causation, no implication or suggestion exists.

  25. #25 ted
    July 20, 2012

    It is worth noting that in my above comment, I state “correlation alone” for a very important reason: It is possible to construct a test whose measurements of correlation do imply causation.

  26. #26 Greg Laden
    July 20, 2012

    What are you trying to imply?

  27. #27 Greg Laden
    July 21, 2012

    Yeah, but did you read anything other than the title of the post?

  28. #28 Lois Matelan
    January 5, 2014

    You seem to have completely ignored the issue of a mechanism. If you can posit a mechanism by which phenomenon A can bring about phenomenon B, doesn’t that give much stronger substantiation to the causal relationship between A and B?

  29. #29 Greg Laden
    January 6, 2014

    Lois, mechanism is everything and this post is all about it!

  30. #30 OTMPut
    April 8, 2014

    “Gravity causes the apple to fall to the ground instead of sideways when it detaches from the tree ”

    Is Gravity “our” explanation for apple falling or did it really “cause” the falling?

  31. #31 Neil Bates
    tyrannogenius.blogspot.com
    April 15, 2014

    How about: “correlation does not necessitate causation”? That doesn’t imply cheap-shot evasion of that it so often does.

  32. #32 Greg Laden
    April 16, 2014

    Neil, that seems reasonable other than the possible confusion about causality of the causality.

    The original problem comes from the use of the word “Imply” in logic vs. “Imply” in vernacular parlance. Like the word “Theory” in science vs. the vernacular they are almost opposites. I imply things in my daily speech that I’m trying to avoid saying, sometimes because I can’t really say it. A implies B in logic because if you see A you should bet on B.

  33. #33 Suzie
    Toronto
    June 23, 2014

    The dismissive nature of the term “correlation does not imply causation” as used on the Internet is not only inane, it is not worth the effort it took to type it.

    Pointing out smugly that “correlation does not imply causation” is incessant on science forums. We see that comment, and cringe, as it is clearly degrading to people that found correlation and want to continue along with the research until they prove causation, or their theory disproves itself.

    It is nauseating to read comments constructed by mental midgets disparaging epidemioligists, statistical analysts, herpetologists, and world renowned disease control scientists with an obscenely misused phrase like “correlation does not imply causation”. Save your flimsy knowledge of statistics and science to impress the freshmen during frosh week.

  34. #34 Neil Bates
    Can I put my blog URL: http://tyrannogenius.blogspot.com
    June 23, 2014

    I think Greg made the point well. To put in my own words: correlation does not *necessitate* causation, but it often does, especially when combined with theoretical reasons to suspect that. Also, remember that technically, in philosophical logic it is a “fallacy” if it is not a necessary logical conclusion, even if very often the case (such as, where there’s smoke, there’s fire – since smoke could come from another source, even bottled up previously etc.) The whole point is to “keep in perspective” and not get hung up on either certainty or be dismissive.

  35. #35 Peter
    London
    July 31, 2014

    “…as it is clearly degrading to people that found correlation and want to continue along with the research until they prove causation.”

    But you can never “prove” casuation. You can increase confidence in your hypothesis, but you will never reach 100%, no matter how much research you do.

    You can’t prove causation because it can never be observed. Even in the case of the apply and gravity mentioned in the blog, we can’t be 100% sure that gravity caused the apple to fall. It’s possible that one day an apple might shoot sidewards instead of falling; it’s possible that that might already have happened, but no-one saw it.

    To prove casuation, you’d have to be omniscient; and we’re not.

  36. #36 Brett
    September 27, 2014

    Correlation does not imply causation means there are possibly other unknown/uncontrolled variables that may be causing the effect you are witnessing. Causational experiments have removed all other possible variables while correlational experiments have not which explains the weaker statement and inability to infer causation.

  37. #37 Greg Laden
    September 27, 2014

    Brett, that’s a possibly correct post hoc explanation but usually does not apply. Most of the time the conversation is not about experiments at all. Correlation doe not suggest uncontrolled variables at all; in a very large number of cases, with high correlations, it would be wrong to assume that there are. So-called “causational methods” don’t so anything to identify cause. Having a high correlation and an excellent understanding of physical causality for a system does not get better by using “causational experimentation” which is little more than normal experimentation where you’ve made explicit statement about cause. Finding a correlation between two variables by surprise provides only weak suggestion of an underlying cause. For this reason, the only real difference between an approach that is “correlational” and one that is “causal” is the level to which one argues against a proposed cause on the basis of a prior incredulity. That is rarely helpful or impressive.

    So no, that’s not what it means, not where the term comes from, or how it is ever used except in a few rare instances, and the distinction between correlational and causal (the latter only used in some subfields but with analogs elsewhere) is not a novel experimental or statistical technique, really, but an approach to structuring analysis, which still requires understanding of (and proof of) mechanism.

  38. #38 Kevin ONeill
    United States
    September 27, 2014

    Greg – I’ve always known the phrase as, ‘Correlation is not causation.’ That’s a fairly straightforward statement in my mind, basically saying, ‘Beware of jumping to conclusions.’

    The useage you cite, ‘Correlation does not imply causation.’ seems quite wrong. It seems obvious to me that correlation *does* imply causation – but it doesn’t prove it.

    Spurious correlations can be quite fun though :)

  39. #39 Greg Laden
    September 28, 2014

    Kevin, I discuss that exact question in detail in the post!