Hmm. I think I know what you’re referring to — aside from prediction, you also need to be able to factor out irrelevant information, consider hypotheticals, and construct causal networks. A world where cause and effect didn’t work a good deal of the time might still be predictable, but choosing actions wouldn’t work very effectively.
(I suspect that if I’d read more of Pearl’s Causality I’d be able to express this more precisely.)
Well, when you use Bayes theorem, you are updating based on a conditioning event. But with causal info, it is not a conditioning event anymore. I don’t think it is literally impossible to be Bayesian with causal info, but it sounds hard. I am still thinking about it.
So I am not sure how practical this “be more Bayesian” advice really is. In practice we should be able to use information of the form “aspirin does not cause cancer”, right?
Hmm. I think I know what you’re referring to — aside from prediction, you also need to be able to factor out irrelevant information, consider hypotheticals, and construct causal networks. A world where cause and effect didn’t work a good deal of the time might still be predictable, but choosing actions wouldn’t work very effectively.
(I suspect that if I’d read more of Pearl’s Causality I’d be able to express this more precisely.)
Is that what you’re getting at, at all?
Well, when you use Bayes theorem, you are updating based on a conditioning event. But with causal info, it is not a conditioning event anymore. I don’t think it is literally impossible to be Bayesian with causal info, but it sounds hard. I am still thinking about it.
So I am not sure how practical this “be more Bayesian” advice really is. In practice we should be able to use information of the form “aspirin does not cause cancer”, right?
[ I did not downvote the parent. ]