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I haven't read any of the cited research, but formalization of causality seems like it should be a straightforward endeavour. The notion of a controlled experi

by dfranke 17y ago

I haven't read any of the cited research, but formalization of causality seems like it should be a straightforward endeavour. The notion of a controlled experiment already embodies a fairly rigorous understanding of what it means: if you have two starting conditions which are identical except for one variable 'A', and the outcomes differ in terms of 'B', then you've established a causal link from A to B. I don't envision any particular difficulty in transcribing this notion into the language of formal logic.

timf 17y ago
His comment about statistics (not formal logic?) is mainly about usefulness. It's not useful to have a pure causality representation because with the real world and other complex systems, you don't know all the variables. Your variable 'A' in 10 years might be shown to be a combination of variables. I, for example, only 99.99% believe in gravity. One day we might float off the planet. Lack of 100% proof about observations was one of Hume's basic arguments and I think the point of the "do-calculus" for AI that is discussed in this link. We all have an internal "do-calculus" for operating in the world: work with current held beliefs/rules until they're proven to work differently, etc.
- darkxanthos 17y ago
  I'm not convinced of this "truth". Do-calculus might help AI in its current state, but really they may just be making up for fundamental flaws in their neural simulations. I normally hate to hear people say no one knows how certain things work but in this instance it seems apt. No one has created a self autonomous consciousness yet and so no one really knows exactly what goes into it. I guess there's also a difference in advancing the study of robots and advancing the study of artificial intelligence.
  - jey 17y ago
    > neural simulations huh? I have no idea what you're talking about.
jerf 17y ago
I am reminded of set theory, or perhaps rather, the history of set theory. Creating "a" set theory is easy. Working through all the implications and trying to create a robust set theory took a lot of time, and involved a few unexpected complications. 'Course it all looks simple now from our 21st century perspective, but it didn't happen overnight. I suspect the theory of causality will be the same way; yeah, a quick approximation that works most of the time is readily available (almost enough information in that post for a mathematically-inclined person to work it out, after all), but working through all the nuances will probably take us somewhere unexpected. For example: What will it mean when someone proves there is no one theory of causality, like there is no one set theory? (Obviously, I don't know this will happen, but from a strictly mathematical point of view it sure wouldn't surprise me much; for a similar example, see Arrow's Impossibility Theorem. (If you (in general, not dfranke specifically) are not math savvy, still, Google it; it's not that hard to follow. Wikipedia's a bit heavy on the math, there are less formal introductions around.)) A branch of math to keep an eye on, I think.
jey 17y ago
Yes, expressing a causal structure is pretty easy (e.g. make a Bayes net). The hard part is inferring a causal structure from a statistical pile of observations. According to Pearl, many statisticians even flatly reject[ed] the notion of causality as just a convenient fiction (iirc; it's been a long time since I read it).

by dfranke 17y ago

timf 17y ago

His comment about statistics (not formal logic?) is mainly about usefulness. It's not useful to have a pure causality representation because with the real world and other complex systems, you don't know all the variables. Your variable 'A' in 10 years might be shown to be a combination of variables. I, for example, only 99.99% believe in gravity. One day we might float off the planet. Lack of 100% proof about observations was one of Hume's basic arguments and I think the point of the "do-calculus" for AI that is discussed in this link. We all have an internal "do-calculus" for operating in the world: work with current held beliefs/rules until they're proven to work differently, etc.

darkxanthos 17y ago
I'm not convinced of this "truth". Do-calculus might help AI in its current state, but really they may just be making up for fundamental flaws in their neural simulations. I normally hate to hear people say no one knows how certain things work but in this instance it seems apt. No one has created a self autonomous consciousness yet and so no one really knows exactly what goes into it. I guess there's also a difference in advancing the study of robots and advancing the study of artificial intelligence.
- jey 17y ago
  > neural simulations huh? I have no idea what you're talking about.

jerf 17y ago

I am reminded of set theory, or perhaps rather, the history of set theory. Creating "a" set theory is easy. Working through all the implications and trying to create a robust set theory took a lot of time, and involved a few unexpected complications. 'Course it all looks simple now from our 21st century perspective, but it didn't happen overnight. I suspect the theory of causality will be the same way; yeah, a quick approximation that works most of the time is readily available (almost enough information in that post for a mathematically-inclined person to work it out, after all), but working through all the nuances will probably take us somewhere unexpected. For example: What will it mean when someone proves there is no one theory of causality, like there is no one set theory? (Obviously, I don't know this will happen, but from a strictly mathematical point of view it sure wouldn't surprise me much; for a similar example, see Arrow's Impossibility Theorem. (If you (in general, not dfranke specifically) are not math savvy, still, Google it; it's not that hard to follow. Wikipedia's a bit heavy on the math, there are less formal introductions around.)) A branch of math to keep an eye on, I think.

jey 17y ago

Yes, expressing a causal structure is pretty easy (e.g. make a Bayes net). The hard part is inferring a causal structure from a statistical pile of observations. According to Pearl, many statisticians even flatly reject[ed] the notion of causality as just a convenient fiction (iirc; it's been a long time since I read it).