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Philosophy 101 would tell us that statistics could capture only correlations, not causation. Causation require different kind of knowledge, of what is beyond ap

Philosophy 101 would tell us that statistics could capture only correlations, not causation. Causation require different kind of knowledge, of what is beyond appearances.

warrenpj 11y ago
When we see that two events are correlated (which we need some kind of statistics to do), we can tell a story (a theory, or an explanation) about how one event causes the other. If the explanation stands up to rational testing over time (where statistics are an important tool), then we have gained knowledge - one plausible explanation of what is "beyond appearances". Therefore statistics are useful both before positing an explanation, and after to falsify it.
- dschiptsov 11y ago
  That theory or explanation requires the domain knowledge I am talking about. Mere statistics about appearances is not enough. To make it clear - statistics is obviously useful. It just cannot infer any proposition like x is y for all values of x.
  - warrenpj 11y ago
    I agree. I should have said that explicitly, before.
nl 11y ago
I just unflagged this comment. While it's actually somewhat wrong, it makes an important point. Statistics can be used to discover a causal relationship. It can't give you an absolute answer, but it can give you a statistical likelihood of the causality. That's a pretty important step forward. That's what this is about.
- dschiptsov 11y ago
  Likehood or probability makes sense only in models where one knows with maximum certainty that he have captured all/every relevant variables, its weights and has all kinds of possible events in a distribution. Otherwise the whole model is mere a story. An illusion. Failure to capture reality adequately is where so-called "black swans" are coming from. This is meaning behind the "correlation is not causation" meme. There is nothing wrong with Bayesian reasoning, except when it is applyed to an inadequate dataset, which is almost always the case. Would you like to elaborate about "somewhat wrong", with quotations from Principles of Mathematics, for example?
  - nl 11y ago
    Would you like to elaborate about "somewhat wrong", with quotations from Principles of Mathematics, for example? It's "somewhat wrong" because in some cases it is possible to derive causation using statistical methods. Have you read the linked book? It should answer your questions. If not I'll point you to Michael Nielsen's post[1], where he explain[s] how the causal calculus can sometimes (but not always!) be used to infer causation from a set of data, even when a randomized controlled experiment is not possible. Also in the post, I’ll describe some of the limits of the causal calculus It's a pretty long post, but the gist of it is that in some circumstances it's possible to build a world model of an imaginary controlled, randomized experiment and then see if non-controlled, real world data matches those expectations. What that gives you is a distribution of the probabilities of causality. [1] http://www.michaelnielsen.org/ddi/if-correlation-doesnt-imply-causation-then-what-does/ http://www.michaelnielsen.org/ddi/if-correlation-doesnt-impl...
    - dschiptsov 11y ago
      The probability calculus has nothing to do with correctness of probabilities supplied the very same way the propositional calculus had nothing to do with validity of given propositions. Or any calculus in that matter. Inference is application of valid heuristics. Mere statistics is not sufficient.
Houshalter 11y ago
It actually is possible to infer causality from statistics. That's (partially) what this author's work is all about. For a brief explanation of how that's possible, see this post: http://lesswrong.com/lw/ev3/causal_diagrams_and_causal_models/ http://lesswrong.com/lw/ev3/causal_diagrams_and_causal_model...

Philosophy 101 would tell us that statistics could capture only correlations, not causation. Causation require different kind of knowledge, of what is beyond appearances.

warrenpj 11y ago

When we see that two events are correlated (which we need some kind of statistics to do), we can tell a story (a theory, or an explanation) about how one event causes the other. If the explanation stands up to rational testing over time (where statistics are an important tool), then we have gained knowledge - one plausible explanation of what is "beyond appearances". Therefore statistics are useful both before positing an explanation, and after to falsify it.

dschiptsov 11y ago
That theory or explanation requires the domain knowledge I am talking about. Mere statistics about appearances is not enough. To make it clear - statistics is obviously useful. It just cannot infer any proposition like x is y for all values of x.
- warrenpj 11y ago
  I agree. I should have said that explicitly, before.

nl 11y ago

I just unflagged this comment. While it's actually somewhat wrong, it makes an important point. Statistics can be used to discover a causal relationship. It can't give you an absolute answer, but it can give you a statistical likelihood of the causality. That's a pretty important step forward. That's what this is about.

dschiptsov 11y ago
Likehood or probability makes sense only in models where one knows with maximum certainty that he have captured all/every relevant variables, its weights and has all kinds of possible events in a distribution. Otherwise the whole model is mere a story. An illusion. Failure to capture reality adequately is where so-called "black swans" are coming from. This is meaning behind the "correlation is not causation" meme. There is nothing wrong with Bayesian reasoning, except when it is applyed to an inadequate dataset, which is almost always the case. Would you like to elaborate about "somewhat wrong", with quotations from Principles of Mathematics, for example?
- nl 11y ago
  Would you like to elaborate about "somewhat wrong", with quotations from Principles of Mathematics, for example? It's "somewhat wrong" because in some cases it is possible to derive causation using statistical methods. Have you read the linked book? It should answer your questions. If not I'll point you to Michael Nielsen's post[1], where he explain[s] how the causal calculus can sometimes (but not always!) be used to infer causation from a set of data, even when a randomized controlled experiment is not possible. Also in the post, I’ll describe some of the limits of the causal calculus It's a pretty long post, but the gist of it is that in some circumstances it's possible to build a world model of an imaginary controlled, randomized experiment and then see if non-controlled, real world data matches those expectations. What that gives you is a distribution of the probabilities of causality. [1] http://www.michaelnielsen.org/ddi/if-correlation-doesnt-imply-causation-then-what-does/ http://www.michaelnielsen.org/ddi/if-correlation-doesnt-impl...
  - dschiptsov 11y ago
    The probability calculus has nothing to do with correctness of probabilities supplied the very same way the propositional calculus had nothing to do with validity of given propositions. Or any calculus in that matter. Inference is application of valid heuristics. Mere statistics is not sufficient.

Houshalter 11y ago

It actually is possible to infer causality from statistics. That's (partially) what this author's work is all about. For a brief explanation of how that's possible, see this post: http://lesswrong.com/lw/ev3/causal_diagrams_and_causal_models/ http://lesswrong.com/lw/ev3/causal_diagrams_and_causal_model...