Pearl’s answer, from IIRC Chapter 7 of Causality, which I find 80% satisfying, is about using external knowledge about repeatability to consider a system in isolation. The same principle gets applied whenever a researcher tries to shield an experiment from outside interference.
This is actually a good illustration of what I mean. You can’t shield an experiment from outside influence entirely, not even in principle, because its you doing the shielding, and your activity is caused by the rest of the world. If you decide to only look at a part of the world, one that doesn’t contain you, thats not a problem—but thats just assuming that that route of influence doesn’t matter. Similarly, “knowledge about repeatability” is causal knowledge. This answer just tells you how to gain causal knowledge of parts of the world, given that you already have some causal knowledge about the whole. So you can’t apply it to the entire world. This is why I say it doesn’t go well with embedded agency.
The second is about limiting allowed interventions.
No? What I’m limiting is what dependencies we’re considering. And it seems that what you say after this is about singular causality, and I’m not really concerned with that. Having a causal web is sufficient for decision theory.
Causal inference has long been about how to take small assumptions about causality and turn them into big inferences about causality. It’s very bad at getting causal knowledge from nothing. This has long been known.
For the first: Well, yep, that’s why I said I was only 80% satisfied.
For the second: I think you’ll need to give a concrete example, with edges, probabilities, and functions. I’m not seeing how to apply thinking about complexity to a type causality setting, where it’s assumed you have actual probabilities on co-occurrences.
This is actually a good illustration of what I mean. You can’t shield an experiment from outside influence entirely, not even in principle, because its you doing the shielding, and your activity is caused by the rest of the world. If you decide to only look at a part of the world, one that doesn’t contain you, thats not a problem—but thats just assuming that that route of influence doesn’t matter. Similarly, “knowledge about repeatability” is causal knowledge. This answer just tells you how to gain causal knowledge of parts of the world, given that you already have some causal knowledge about the whole. So you can’t apply it to the entire world. This is why I say it doesn’t go well with embedded agency.
No? What I’m limiting is what dependencies we’re considering. And it seems that what you say after this is about singular causality, and I’m not really concerned with that. Having a causal web is sufficient for decision theory.
Causal inference has long been about how to take small assumptions about causality and turn them into big inferences about causality. It’s very bad at getting causal knowledge from nothing. This has long been known.
For the first: Well, yep, that’s why I said I was only 80% satisfied.
For the second: I think you’ll need to give a concrete example, with edges, probabilities, and functions. I’m not seeing how to apply thinking about complexity to a type causality setting, where it’s assumed you have actual probabilities on co-occurrences.