7 ms·

Causality has never been of much interest to me. I never understood why others make such a big deal out of it. Maybe it is a real thing, maybe illusion, definit

by nonbel 10y ago

Causality has never been of much interest to me. I never understood why others make such a big deal out of it. Maybe it is a real thing, maybe illusion, definitely some kind of heuristic... but shouldn't we be searching for useful "laws" (eg F ~ m1*m2/r^2) rather than "cause"? I know some have denied causality a place in science: "In the following paper I wish, first, to maintain that the word "cause" is so inextricably bound up with misleading associations as to make its complete extrusion from the philosophical vocabulary desirable; secondly, to inquire what principle, if any, is employed in science in place of the supposed "law of causality" which philosophers imagine to be employed; thirdly, to exhibit certain confusions, especially in regard to teleology and determinism, which appear to me to be connected with erroneous notions as to causality." http://www.hist-analytic.com/Russellcause.pdf http://www.hist-analytic.com/Russellcause.pdf

epistasis 10y ago
As usual, Pearl has some good writing on this: http://bayes.cs.ucla.edu/BOOK-2K/causality2-epilogue.pdf http://bayes.cs.ucla.edu/BOOK-2K/causality2-epilogue.pdf >Take, for instance, Newton’s law: >f = ma. >The rules of algebra permit us to write this law in a wild variety of syntactic forms, all meaning the same thing – that if we know any two of the three quantities, the third is determined. >Yet, in ordinary discourse we say that force causes acceleration – not that accelera- tion causes force, and we feel very strongly about this distinction. >Likewise, we say that the ratio f/a helps us determine the mass, not that it causes the mass. >Such distinctions are not supported by the equations of physics, and this leads us to ask whether the whole causal vocabulary is purely metaphysical, “surviving, like the monarchy . . .”. >Fortunately, very few physicists paid atten- tion to Russell’s enigma. They continued to write equations in the office and talk cause–effect in the cafeteria; with astonishing success they smashed the atom, invented the transistor and the laser.
- nonbel 10y ago
  How about: In ordinary discourse we say that "the earth orbits the sun" – not that "the sun orbits the earth", and we feel very strongly about this distinction. It just seems like discussion of "what causes what" is missing the point, just like the above argument. That one persisted for centuries (millenia?) until it was pointed out the answer is neither! It depends on your reference frame.
  - epistasis 10y ago
    Causality is quite different from frame of reference. Lets return to your equation. r^2 = m1*m2/F is a statement of correlation that's valid to many digits, that also says that "these are all the variables that affect anything here." It allows for calculations of quantities by plugging in numbers. In combination with other equations, it allows extrapolitaion and physical thinking. But equations alone are not enough for a physicist to make progress on problems, they don't combine arbitrarily, they still use that famous "physical intuition" that turns out to involve a heck of a lot of causality that they talk about in the cafeteria but don't often write about. In the world outside of physics classrooms, most situations involve a huge number of variables interacting with each other, and the variables themselves are leaky abstractions. This means that the correlation of quantities does not go out to many digits, or even one digit. This does not make an equation a useful abstraction. People have developed probabilistic models that stand in for equations in these situations. Sure, it would nice to have an equation that modeled things like they do in the simple situations that Physics publishes on, but when the equations of physics are so weak that they can't even fold a protein, how are we going to use them to model the interaction of a potential drug with a protein? Equations are great, when they apply. By introducing simplifying assumptions, we can sometimes extend their utility into new domains, but at the cost of introducing fragility because of those simplifying assumptions. They allow for calculating new things with only a very few computations. But nearly every new field of science invents new math. One of the more recent inventions is causality. It works fantastic in those sciences that use experimental interventions. Simple algebraic equations do not.
    - nonbel 10y ago
      Sorry, all these arguments seem like non-sequiturs to me. Physicists might pray together in the cafeteria to come up with good ideas. It doesn't mean God answered their prayers when they have one. Useful laws need not be exactly correct. You can keep adjusting to stay on course to account for the error due to any simplifications. Sure causality seems like it must be at least some kind of heuristic, that doesn't mean looking for causes is the best, or even a good, use of time.
      - 10y ago
pinouchon 10y ago
I'd say that causality seems useful for intelligence. Josh Tenenbaum has pushed the idea of causal models and inference, and I find it compelling > Recent progress in artificial intelligence (AI) has renewed interest in building systems that learn and think like people. Many advances have come from using deep neural networks trained end-to-end in tasks such as object recognition, video games, and board games, achieving performance that equals or even beats humans in some respects. Despite their biological inspiration and performance achievements, these systems differ from human intelligence in crucial ways. > We review progress in cognitive science suggesting that truly human-like learning and thinking machines will have to reach beyond current engineering trends in both what they learn, and how they learn it. Specifically, we argue that these machines should (a) build causal models of the world that support explanation and understanding, rather than merely solving pattern recognition problems; [...] https://arxiv.org/pdf/1604.00289.pdf https://arxiv.org/pdf/1604.00289.pdf Some others papers related to these ideas: - Learning a Theory of Causality http://stanford.edu/~ngoodman/papers/LTBC_psychreview_final.pdf http://stanford.edu/~ngoodman/papers/LTBC_psychreview_final.... - Theory-Based Causal Induction: http://cocosci.berkeley.edu/tom/papers/tbci.pdf http://cocosci.berkeley.edu/tom/papers/tbci.pdf -
- nonbel 10y ago
  I do accept engineering feats as evidence people know what they are talking about. Are there any tasks where one of these "causality-aware" robots [algorithms] outperforms "pattern-recognition" robots? From the brief summary it sounds very hypothetical, especially compared to the causality-agnostic pattern-recognition approach which I have seen work surprisingly well. That group has convinced me they are on to something quite big.
  - pinouchon 10y ago
    I agree that in practise, deep learning tend to perform very well. With that said, it is not without limitations. In general, model-based machine learning approaches do not perform as well, but we should not limit our assessment only to performance. For example in this review: https://arxiv.org/pdf/1605.01138.pdf https://arxiv.org/pdf/1605.01138.pdf, a model-based approach is compared against a deep learning approach. It is very interesting to me that even if deep learning performs better, its failure cases are very different than human failure cases (deep learning is not sensitive to "physics illusions"). Whereas the model-based approach is very much in-line with human performance characteristics. Also, on this task (physical scene understanding), deep learning generalises very poorly.
nerdponx 10y ago
It's critically important whenever you are being asked to develop a policy of some kind, whether it's public policy or a business procedure. If you only understand relationships and not the causal direction relationships, you can end up making very bad decisions. E.g., "black men drop out of school more often than white men, therefore we shouldn't waste time educating black men."
- dgfgfdagasdfgfa 10y ago
  Sure, but causality is ridiculously easy to get wrong, and very rarely aligns with intuition. Even the expectation it exists can mislead you.
- nonbel 10y ago
  But the alternative I proposed is coming up with quantitative laws, not vague astrology like "Race A does this more often than Race B, therefore we need to do less of C". That is an interesting connection to make though. If people view wild speculation as the alternative focus of research, incorporating the concept of causality would at least slow them down. This would reduce the amount of misinformation generated and number of destructive policies implemented. Still, I think causality offers more than that.
  - epistasis 10y ago
    > the alternative I proposed is coming up with quantitative laws, not vague astrology like "Race A does this more often than Race B, therefore we need to do less of C". You appear to be saying that causality is not quantitative, however that is not true.
    - Retra 10y ago
      No, they're saying the causal relation does not add much more (quantitatively speaking) to an existing correlative relation. They're suggesting we throw out the part that distinguishes causal from correlative, not correlative reasoning altogether.
nl 10y ago
Rule mining[1] is a thing. It's very useful. But it has a weakness - it doesn't always understand causality. As you note, much of the time this doesn't matter. A good counterexample is the story of scurvy prevention[2]. Back in 1747, the British Royal Navy proved that scurvy could be prevented by eating citrus. The Navy started serving all sailors lime juice, and sure enough scurvy stopped occurring. Fast forward to 1911. Scientists had developed rules which matched the observed behavior of scurvy very well: Atkinson inclined to Almroth Wright’s theory that scurvy is due to an acid intoxication of the blood caused by bacteria.. There was little scurvy in Nelson’s days; but the reason is not clear, since, according to modern research, lime-juice only helps to prevent it. We had, at Cape Evans, a salt of sodium to be used to alkalize the blood as an experiment, if necessity arose. Darkness, cold, and hard work are in Atkinson’s opinion important causes of scurvy. Basically, what had happened was that widespread steam travel and a process for canning lime juice were developed roughly simultaneously. The canned lime juice no longer provided vitamin C, but no one realized this was a problem because sea voyages were much shorter so scurvy never developed. That lack of a correct causality model meant that when polar exploration happened, scurvy started occurring again and no one knew why. [1] https://www.mapr.com/blog/association-rule-mining-not-your-typical-data-science-algorithm https://www.mapr.com/blog/association-rule-mining-not-your-t... [2] http://idlewords.com/2010/03/scott_and_scurvy.htm http://idlewords.com/2010/03/scott_and_scurvy.htm
- Retra 10y ago
  But your explanation of the problem here is that nobody recognized the other relevant correlations well enough to rule them out.
  - lappa 10y ago
    His example demonstrates that determining causality avoids problems such as the inability to recognize other relevant correlations.
  - nl 10y ago
    Yes. But my point was to show that causality is important, which the OP appears to doubt altogether.
srean 10y ago
Causality is important when you want to design interventions. A non-causal regression model on {Y={CA, no CA}, X ={Blonde hair, dark hair}} may be able to replicate the observed effect that blonde hair and skin cancer go together. However you cannot use that regression coefficient for coming up with an intervention. Dyeing the hair is not going to reduce cancer risk although the regression model will predict exactly that.

by nonbel 10y ago

epistasis 10y ago

As usual, Pearl has some good writing on this: http://bayes.cs.ucla.edu/BOOK-2K/causality2-epilogue.pdf http://bayes.cs.ucla.edu/BOOK-2K/causality2-epilogue.pdf >Take, for instance, Newton’s law: >f = ma. >The rules of algebra permit us to write this law in a wild variety of syntactic forms, all meaning the same thing – that if we know any two of the three quantities, the third is determined. >Yet, in ordinary discourse we say that force causes acceleration – not that accelera- tion causes force, and we feel very strongly about this distinction. >Likewise, we say that the ratio f/a helps us determine the mass, not that it causes the mass. >Such distinctions are not supported by the equations of physics, and this leads us to ask whether the whole causal vocabulary is purely metaphysical, “surviving, like the monarchy . . .”. >Fortunately, very few physicists paid atten- tion to Russell’s enigma. They continued to write equations in the office and talk cause–effect in the cafeteria; with astonishing success they smashed the atom, invented the transistor and the laser.

nonbel 10y ago
How about: In ordinary discourse we say that "the earth orbits the sun" – not that "the sun orbits the earth", and we feel very strongly about this distinction. It just seems like discussion of "what causes what" is missing the point, just like the above argument. That one persisted for centuries (millenia?) until it was pointed out the answer is neither! It depends on your reference frame.
- epistasis 10y ago
  Causality is quite different from frame of reference. Lets return to your equation. r^2 = m1*m2/F is a statement of correlation that's valid to many digits, that also says that "these are all the variables that affect anything here." It allows for calculations of quantities by plugging in numbers. In combination with other equations, it allows extrapolitaion and physical thinking. But equations alone are not enough for a physicist to make progress on problems, they don't combine arbitrarily, they still use that famous "physical intuition" that turns out to involve a heck of a lot of causality that they talk about in the cafeteria but don't often write about. In the world outside of physics classrooms, most situations involve a huge number of variables interacting with each other, and the variables themselves are leaky abstractions. This means that the correlation of quantities does not go out to many digits, or even one digit. This does not make an equation a useful abstraction. People have developed probabilistic models that stand in for equations in these situations. Sure, it would nice to have an equation that modeled things like they do in the simple situations that Physics publishes on, but when the equations of physics are so weak that they can't even fold a protein, how are we going to use them to model the interaction of a potential drug with a protein? Equations are great, when they apply. By introducing simplifying assumptions, we can sometimes extend their utility into new domains, but at the cost of introducing fragility because of those simplifying assumptions. They allow for calculating new things with only a very few computations. But nearly every new field of science invents new math. One of the more recent inventions is causality. It works fantastic in those sciences that use experimental interventions. Simple algebraic equations do not.
  - nonbel 10y ago
    Sorry, all these arguments seem like non-sequiturs to me. Physicists might pray together in the cafeteria to come up with good ideas. It doesn't mean God answered their prayers when they have one. Useful laws need not be exactly correct. You can keep adjusting to stay on course to account for the error due to any simplifications. Sure causality seems like it must be at least some kind of heuristic, that doesn't mean looking for causes is the best, or even a good, use of time.
    - 10y ago

pinouchon 10y ago

I'd say that causality seems useful for intelligence. Josh Tenenbaum has pushed the idea of causal models and inference, and I find it compelling > Recent progress in artificial intelligence (AI) has renewed interest in building systems that learn and think like people. Many advances have come from using deep neural networks trained end-to-end in tasks such as object recognition, video games, and board games, achieving performance that equals or even beats humans in some respects. Despite their biological inspiration and performance achievements, these systems differ from human intelligence in crucial ways. > We review progress in cognitive science suggesting that truly human-like learning and thinking machines will have to reach beyond current engineering trends in both what they learn, and how they learn it. Specifically, we argue that these machines should (a) build causal models of the world that support explanation and understanding, rather than merely solving pattern recognition problems; [...] https://arxiv.org/pdf/1604.00289.pdf https://arxiv.org/pdf/1604.00289.pdf Some others papers related to these ideas: - Learning a Theory of Causality http://stanford.edu/~ngoodman/papers/LTBC_psychreview_final.pdf http://stanford.edu/~ngoodman/papers/LTBC_psychreview_final.... - Theory-Based Causal Induction: http://cocosci.berkeley.edu/tom/papers/tbci.pdf http://cocosci.berkeley.edu/tom/papers/tbci.pdf -

nonbel 10y ago
I do accept engineering feats as evidence people know what they are talking about. Are there any tasks where one of these "causality-aware" robots [algorithms] outperforms "pattern-recognition" robots? From the brief summary it sounds very hypothetical, especially compared to the causality-agnostic pattern-recognition approach which I have seen work surprisingly well. That group has convinced me they are on to something quite big.
- pinouchon 10y ago
  I agree that in practise, deep learning tend to perform very well. With that said, it is not without limitations. In general, model-based machine learning approaches do not perform as well, but we should not limit our assessment only to performance. For example in this review: https://arxiv.org/pdf/1605.01138.pdf https://arxiv.org/pdf/1605.01138.pdf, a model-based approach is compared against a deep learning approach. It is very interesting to me that even if deep learning performs better, its failure cases are very different than human failure cases (deep learning is not sensitive to "physics illusions"). Whereas the model-based approach is very much in-line with human performance characteristics. Also, on this task (physical scene understanding), deep learning generalises very poorly.

nerdponx 10y ago

It's critically important whenever you are being asked to develop a policy of some kind, whether it's public policy or a business procedure. If you only understand relationships and not the causal direction relationships, you can end up making very bad decisions. E.g., "black men drop out of school more often than white men, therefore we shouldn't waste time educating black men."

dgfgfdagasdfgfa 10y ago
Sure, but causality is ridiculously easy to get wrong, and very rarely aligns with intuition. Even the expectation it exists can mislead you.
nonbel 10y ago
But the alternative I proposed is coming up with quantitative laws, not vague astrology like "Race A does this more often than Race B, therefore we need to do less of C". That is an interesting connection to make though. If people view wild speculation as the alternative focus of research, incorporating the concept of causality would at least slow them down. This would reduce the amount of misinformation generated and number of destructive policies implemented. Still, I think causality offers more than that.
- epistasis 10y ago
  > the alternative I proposed is coming up with quantitative laws, not vague astrology like "Race A does this more often than Race B, therefore we need to do less of C". You appear to be saying that causality is not quantitative, however that is not true.
  - Retra 10y ago
    No, they're saying the causal relation does not add much more (quantitatively speaking) to an existing correlative relation. They're suggesting we throw out the part that distinguishes causal from correlative, not correlative reasoning altogether.

nl 10y ago

Rule mining[1] is a thing. It's very useful. But it has a weakness - it doesn't always understand causality. As you note, much of the time this doesn't matter. A good counterexample is the story of scurvy prevention[2]. Back in 1747, the British Royal Navy proved that scurvy could be prevented by eating citrus. The Navy started serving all sailors lime juice, and sure enough scurvy stopped occurring. Fast forward to 1911. Scientists had developed rules which matched the observed behavior of scurvy very well: Atkinson inclined to Almroth Wright’s theory that scurvy is due to an acid intoxication of the blood caused by bacteria.. There was little scurvy in Nelson’s days; but the reason is not clear, since, according to modern research, lime-juice only helps to prevent it. We had, at Cape Evans, a salt of sodium to be used to alkalize the blood as an experiment, if necessity arose. Darkness, cold, and hard work are in Atkinson’s opinion important causes of scurvy. Basically, what had happened was that widespread steam travel and a process for canning lime juice were developed roughly simultaneously. The canned lime juice no longer provided vitamin C, but no one realized this was a problem because sea voyages were much shorter so scurvy never developed. That lack of a correct causality model meant that when polar exploration happened, scurvy started occurring again and no one knew why. [1] https://www.mapr.com/blog/association-rule-mining-not-your-typical-data-science-algorithm https://www.mapr.com/blog/association-rule-mining-not-your-t... [2] http://idlewords.com/2010/03/scott_and_scurvy.htm http://idlewords.com/2010/03/scott_and_scurvy.htm

Retra 10y ago
But your explanation of the problem here is that nobody recognized the other relevant correlations well enough to rule them out.
- lappa 10y ago
  His example demonstrates that determining causality avoids problems such as the inability to recognize other relevant correlations.
- nl 10y ago
  Yes. But my point was to show that causality is important, which the OP appears to doubt altogether.

srean 10y ago

Causality is important when you want to design interventions. A non-causal regression model on {Y={CA, no CA}, X ={Blonde hair, dark hair}} may be able to replicate the observed effect that blonde hair and skin cancer go together. However you cannot use that regression coefficient for coming up with an intervention. Dyeing the hair is not going to reduce cancer risk although the regression model will predict exactly that.