Yeah, I think the mistake there is mostly one of emphasis. In terms of narrowing-down-model-space, most of the work (i.e. most of the model-elimination) is in reconstructing the undirected graphical structure; figuring out which direction each arrow goes is relatively easy after that. (You could view this as a consequence of Science In A High Dimensional World: the hard step is figuring out which handful of variables are directly relevant, and after that it’s relatively easy to experiment with those variables.)
Also, Pearl himself was operating in a statistical framework closer to 20th century frequentism than modern Bayesian—e.g. his algorithms in Causality were designed to use an independence oracle rather than Bayesian model comparison. It turns out that model testing in high-dimensional systems is one of the places where the advantage of modern Bayesianism is largest.
In terms of narrowing-down-model-space, most of the work (i.e. most of the model-elimination) is in reconstructing the undirected graphical structure; figuring out which direction each arrow goes is relatively easy after that.
This depends a lot on the field, no? If it’s too expensive or unethical to intervene, or one plain and simply doesn’t understand the variables well enough to intervene, then figuring out the direction of the causal arrows can be difficult.
It turns out that model testing in high-dimensional systems is one of the places where the advantage of modern Bayesianism is largest.
Interesting, do you have any additional resources on this?
Yeah, I think the mistake there is mostly one of emphasis. In terms of narrowing-down-model-space, most of the work (i.e. most of the model-elimination) is in reconstructing the undirected graphical structure; figuring out which direction each arrow goes is relatively easy after that. (You could view this as a consequence of Science In A High Dimensional World: the hard step is figuring out which handful of variables are directly relevant, and after that it’s relatively easy to experiment with those variables.)
Also, Pearl himself was operating in a statistical framework closer to 20th century frequentism than modern Bayesian—e.g. his algorithms in Causality were designed to use an independence oracle rather than Bayesian model comparison. It turns out that model testing in high-dimensional systems is one of the places where the advantage of modern Bayesianism is largest.
This depends a lot on the field, no? If it’s too expensive or unethical to intervene, or one plain and simply doesn’t understand the variables well enough to intervene, then figuring out the direction of the causal arrows can be difficult.
Interesting, do you have any additional resources on this?