We know correlation does not imply causality. What does?

http://rgrig.blogspot.com/2014/07/predictive-causality.html

by pascal_cuoq • 12 years ago

22 36 12 years ago

rgrig.blogspot.com

StephenGL3• 12 years ago

Wants to be smart, but uses "begs the question" in the first couple of paragraphs. What else didn't get caught in editing?

pascal_cuoq• 12 years ago

This is a post on a personal blog by a brilliant young man who does not appear to be a native English speaker.

Assume that nobody edited or even fact-checked his post. Only judge the ideas there on their own merits.

scoofy2• 12 years ago

Those of us who studied philosophy seriously can tell from the content of the article that the author has not. Thus, it's pretty trivial that he misuses a technical logic term.

TallGuyShort• 12 years ago

Wants to sound smart, so tries to discredit the author with irrelevant criticism and pedantry.

StephenGL3• 12 years ago

Correct, I was a bit too narrow with my criticism. This article is a complete mess, not just that one phrase. It adds very little to a discussion of this topic and is all around poorly formed as is foreshadowed by a poor phrase choice early in the article.

DanBC2• 12 years ago

Begs the question has established another use.

Pedants may want to switch to petitio principii.

You received downvotes because your comment is a middlebrow dismissal. You sieze a single snippet and use that to dismiss the entire submission.

StephenGL3• 12 years ago

Have you read the rest of the submission? It is likewise a bit of a mess. I believe that my assessment informed partly by this item was more or less accurate.

_delirium• 12 years ago

The book Causality by Judea Pearl is kind of an extended answer to this question.

mjklin• 12 years ago

Also much of the work of David Hume

http://en.m.wikipedia.org/wiki/David_Hume#Causation

scoofy2• 12 years ago

Yes, the author is describing the problem of induction. There are two major works of Hume that take on the subject, but the most recent popular work I've read is the black swan by nassim taleb.

I'm on my phone now, but I could give a short summery of the problem, and how I think it is intractable if people are interested.

danieltillett2• 12 years ago

You know correlation implies causality when you can make money from the correlation.

alok-g5• 12 years ago

Well, who knows. You may be deriving money from a different correlation than what you thought. In other words, the cause of money-generation may not be the correlation you thought it is.

For example, you may be making money from your customers' lack of understanding of correlation and causation. You may convince your customers that your product leads to revenues for them when it doesn't. And now the money you derive may be correlated with the strength of their beliefs in your product being correlated to their revenues, but not with your product actually being correlated to their revenues.

danieltillett2• 12 years ago

I must remind myself to not try and be subtle here on HN. The true cause(s) of any effect can never be determined through observation (this is Hume’s problem of induction).

When dealing with causation there is no way around the step of having to propose a causative model that you believe to be true - the point I was trying to make is there is nothing better than making money to convince people that their own model is the correct one. Making money does not mean the model is correct, just that the person making money will believe it to be correct.

QuantumChaos3• 12 years ago

You always need to make some assumption about the causal process before you can infer causality from evidence.

For example, take a randomized trial, where half of patients are given a drug, and half a placebo. If we define Y to be the outcome of interest, and X to be 0 when the patient received the placebo and 1 when they received the drug, then a positive outcome would be a correlation between X and Y. A person might object that "correlation does not imply causation", however, we know enough about the causal process involved, to infer that if Y and X are correlated, this must be because X caused Y.

There are many other ways that causation can be inferred, such as instrumental variables, regression discontinuity, or panel regressions. However all these methods require making an assumption about the causal process underlying the statistical distribution. Whether these assumptions are correct is often a matter for debate.

alok-g5• 12 years ago

Well, that's the point! Correlation is seen in the data, then causation calls to be tested for. Your test for it, and continue to see correlation. Yet, the causation stays as an "assumption" that is a matter of debate. This brings back the question, how do you establish causation then?

Your argument is basically saying that if there is understandable physics behind the correlation, then causation could be safely inferred. However, that does not ultimately answer the question still, since how would physics would have established that explanation to begin with.

QuantumChaos3• 12 years ago

Causation is not a fundamental notion for me.

We happen to live in a world where sometimes things can be ascribed specific causes, but that is due to the nature of the physical universe, not an abstract truth.

Causality in our best theories of physics (general relativity, quantum field theory) is extremely complex and subtle. But that isn't needed anyway, what we use in ordinary reasoning is "naive" physics, an approximation of physical reality. I don't think this "naive physics", which we could also call common sense, can be derived from a statistical model, although maybe it can.

Going back to the randomized trial, presumably the choice to give a drug to a patient vs a placebo was determined by a random number generator. This in turn is a physical process, and there is no reason why the physical process that generates this number could not also influence a person's health outcome. It is only our naive physics model that tells us that there is no such process, and the only way the random number generator affects the health outcome, is through the choice of drug.

So to summarize, doing causal inference relies on a model of reality, and this model is sufficiently complex that the model itself cannot (for now) be derived through purely statistical means, but rather is arrived at through ordinary human thought.

bo1024• 12 years ago

Hmm, this seems to assume that we only have access to collected data (e.g. epidemiological studies) rather than being able to generate the data ourselves (many other types of scientific studies).

There is a key difference. A million surveys of smokers and cancer patients will never definitively prove causality. But if we could (legally and ethically) construct a randomized experiment in which we assigned a random half of the participants to smoke, we could easily conclude with very high confidence that smoking causes cancer.

(If this is not self-evident to you, the reason is straightforward probability. Suppose smoking does not cause cancer. Then when you take any given person and have them start smoking, their probability of getting cancer does not go up. Then the rates of cancer in the smoking group are exactly what they would have been had they not smoked. Then, since the people were placed in groups at random, with very high probability the rate of cancer in the smoking group will be almost the same as in the nonsmoking group. So if we find in our experiment that the rates of cancer are much higher in the smoking group, we must reject the hypothesis that smoking does not cause cancer.)

glesica• 12 years ago

Agreed, this is also a problem in economics (though even in economics there are some limited experiments that can be done). I think a key issue people overlook in this debate is the presence of a theory that makes sense. If you just plot data until you spot something that looks like a trend, or fit models until your F-test comes back significant or your R^2 is big enough, then yeah, you can't even make a good case for causation. But if you start with a reasonable theory that fits with existing knowledge, then you can at least make a case.

afafsd• 12 years ago

But, in the end, we'll never be absolutely sure that one thing causes another.

Maybe so, but in the same pedantic sense that we'll never be "absolutely sure" that the universe didn't pop into existence five seconds ago with our memories already programmed in. This level of pedantry is great for impressing your fellow freshman philosophy students, but not actually useful for real life.

For actual practical day-to-day purposes knowing whether something causes something else is really useful, so what can we do? Controlled experiments. If you want to know whether cyanide is poisonous, get a hundred undergrads, randomly select half of them, feed 'em cyanide and see which ones drop dead. If you observe a strong correlation between the variable you're interested in and the one you think you're controlling then you've established causation for all practical purposes.

The pedant will point out there are two other possible explanations:

1. A massive coincidence -- those fifty undergrads just happened to drop dead for unrelated reasons. You can compute the probability of this as something like the average rate of spontaneous and inexplicable human death in undergrads to the power of fifty and multiplied by 1/(100 choose 50) [or something].

2. A slightly less massive combined with a reverse-causal process. Fifty of your undergrads just happened to be about to die anyway, and some aura emanating from them affected your coin-flipping process. The probability of this is harder to quantify, though since the scenario still requires fifty cases of spontaneous undergraduate death in a short space of time we can put an upper bound on it.

javert3• 12 years ago

But, in the end, we'll never be absolutely sure that one thing causes another.

This is incorrect, because all knowledge is contextual.

For instance, it will always be true that gravity causes things to fall to the Earth. Future knowledge can't invalidate that, in can only expand the context.

Another example: Suppose a primitive tribesman climbes a tall tree, looks all around as far as he can see, and proclaims, "The land is flat." The eventual discovery that Earth is round does not invalidate his claim; it expands the context. The context of "the land [I can see]" is not the same as the context of "the entire Earth."

MichaelBurge• 12 years ago

Your comment about the tribesman reminds me of this essay by Isaac Asimov: http://chem.tufts.edu/answersinscience/relativityofwrong.htm

He makes the great point that the tribesman isn't too far off from the truth: The earth is flat if its curvature is zero, but the 'truth' is that the curvature is almost zero(and the difference isn't measurable by a tribesman).

javert3• 12 years ago

Thanks a lot for sharing this. I wasn't aware of it, and really enjoyed it.

alok-g5• 12 years ago

I am not sure actually that gravity "causes" things to fall to the earth. Gravity is the "name" with give to an entity in a mathematical model that somehow fits the observations of things falling very well. What is the "cause" as to why things fall down, AFAIK, that question is still ultimately unanswerable beyond what would be just semantics. Having found the model though, it would be now correct to say that if someone brings a meteor to the field of the earth (cause), the meteor will fall down (effect), as far as the model is correct.

I am not yet seeing how your second example is relevant to figuring how to establish causation.

javert3• 12 years ago

AFAIK, that question is still ultimately unanswerable beyond what would be just semantics.

Out of my range of expertise but I think this could be said about all knowledge. For instance, about elementary particles, You can always say, "What if there is one lower level that we can't detect?" And the right take-away is not: "Therefore, we never know the cause of anything." The right take-away is that "causation" has to take into account contextuality. You can make lots of X causes Y statements that are valid, as long as you specify the context.

The second example is relevant because it's another illustration that knowledge is contextual. Though it's not about causation. The connection is that I'm trying to use the contextuality of knowledge to defend causation. So I was giving a "pure" example of the contextuality of knowledge that doesn't also involve the complexity of causation.

spikels• 12 years ago

4. It does not account for Selection Bias. Just because some particular sample show a correlation does not mean the true distribution is correlated. This is almost certainly true if you look at lots (say 10+) relationships.

It's just too easy to fool yourself. For every time someone claimed no causality there are dozens, perhaps hundreds, of cases of false claims of causality. There is a huge bias.

The true test of causality is reproducible experiments. If X causes Y then making X happen should make Y much more likely. Unfortunately reality is not so easy to arrange.

drakaal• 12 years ago

May not, not does not.

Correlation and causation can be implied if you have a control. Or if you can rule out external factors. Saying it does not is not accurate.

The parrot wakes and sleeps with the sunrise and sunset. Does the Parrot Cause the sunrise? No. This happened BEFORE the parrot was born. Does the sunset cause the sleep? Throw a blanket over it's cage it goes to sleep. TADA! Causation!

When you have two things which you can't control, and that have been going on longer than you can observe it gets to be harder. Proving the Moon phase causes the Tides, not the Tides cause the moon phase. That's a bit trickier. Not in today's age, but at one point. There is a reason we thought the Earth was flat, and that you could throw a stone in to a pond and get a Turtle.

brownbat• 12 years ago

We keep falling back on Hume as if no one has thought critically about the subject since science became more mature. For the curious, Mackie's "The Cement of the Universe" from 1974 provides a more recent analysis of causation. http://books.google.com/books/about/The_Cement_of_the_Univer...

For a sample, suppose a barn burns down right after someone chucks a lighted match in the barn. You can't say that the match was "necessary," the barn could have been struck by lightning or arson and still burned. And the match wasn't "sufficient," because the match probably also needed to fall upon something flammable, like dry hay; there had to be oxygen; etc.

Mackie comes up with a fancy acronym, INUS, but I'll leave unpacking all that aside. One of the more provocative suggestions is that a "cause" is highly contingent on context (that might only be relevant to human observers).

If people harmlessly throw matches into a concrete-floored barn every day, if it's widely accepted as hot match disposal, but one day some joker uses it to store a bunch of oily rags... well, the unusual factor tends to get the blame.

kazinator• 12 years ago

Oh, correlation does point to causality. What you don't immediately know is where to put the causal arrows. A and B occur together with a high probability: does A cause B, or B cause A? Or do they have a common cause?

Mere statistics cannot do that detective work for us, I'm afraid. But when a pure cause-and-effect relationship is uncovered and reproduced, the correlation will be 100%: remove the cause, the effect disappears. Restore the cause, and the effect appears reliably.

For instance, if we stop the flow of electric current through a coil, the magnetic field will collapse soon afterward. If we start the current again, the field comes back. Moreover, if we place a coil into a similar magnetic field, that field doesn't produce current. These things are 100% reproducible. It's not some weak statistic like "in coils where current was present, the magnetic field was observed to be 3.5% stronger on average". That kind of situation shows that some effects which are not being controlled for are masking the underlying causes and effects.

alok-g5• 12 years ago

Exactly, correlation does "point to" causality. The question is what "establishes" it.

Also, agreed, use of controlled experiments is probably the best we can do to validate the hypothesis, and then assume its correctness beyond doubt till counter examples show up, if at all.

Correlation does not need to be 100% though. We live in the world of quantum mechanics today where you can successfully predict the probability distribution, but not beyond (at least as yet).

srinathh• 12 years ago

Correlation provides a starting point to investigate causality. To establish causality what you do is develop hypothesis for mechanisms of causality, identify intermediate events that may be observable if the mechanism hypothesis is valid and observe for those events. If these are observed, you have a much higher likelihood of causality. You'll typically do this if the return on investment from establishing causality is high enough since it's much more work - eg. You're trying to get a drug approval from FDA or you're trying to justify a major investment decision.

firebrand392• 12 years ago

I have been privately lumping causation and correlation together for decades now. It freed my thinking. While the two are certainly different, it is also true that causation does not have the set-in-stoneness/invariability that is commonly taken for granted. Witness the recent discussion about nasa's impossible-space-propulsion.

In terms of a human acquiring knowledge, correlation certainly is the first step. It is also called observation. Causation just seems to be a subsequent underpinning with context and concepts.

DanBC2• 12 years ago

How do you protect yourself against bias caused by coincidence?

firebrand392• 12 years ago

Well, in the first place I cannot. I take the thing as it is. This is the risk-taking part. But, I guess, that is why I have to move from correlation to causation or rather embed this one correlation into a broader context. Because it protects me against correlational flukes.

Thanks for this inspiring question.

contingencies• 12 years ago

Err ... reliable experimental repetition of the hypothesized results against a control (and/or variety of controls) in carefully isolated environments using carefully controlled and documented methods that effectively remove all other plausible explanations?

Yes, we will never know for sure because https://en.wikipedia.org/wiki/List_of_unsolved_problems_in_p... ... but we can be pretty certain.

We know correlation does not imply causality. What does?

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