I think the lesson a lot of people took away from Pearl is (roughly speaking) that you should look for colliders between independent variables, since colliders are the one primitive causal structure that looks distinct (whereas forward mediators, backward mediators, and confounders look the same from a graphical independence perspective).
My impression is that looking for colliders doesn’t really work for most practical causal inference. So this then translates to Pearl’s approaches not working in practice.
Except I think Pearl has become (always was?) pretty skeptical about inferring causal structure from this sort of data (even though it’s “theoretically” possible), so it might just be a miscommunication along the way. In the rationalist community, this miscommunication probably originates mainly from Eliezer’s endorsement of searching for colliders as one of the main forms of causal inference.
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?
I think the lesson a lot of people took away from Pearl is (roughly speaking) that you should look for colliders between independent variables, since colliders are the one primitive causal structure that looks distinct (whereas forward mediators, backward mediators, and confounders look the same from a graphical independence perspective).
My impression is that looking for colliders doesn’t really work for most practical causal inference. So this then translates to Pearl’s approaches not working in practice.
Except I think Pearl has become (always was?) pretty skeptical about inferring causal structure from this sort of data (even though it’s “theoretically” possible), so it might just be a miscommunication along the way. In the rationalist community, this miscommunication probably originates mainly from Eliezer’s endorsement of searching for colliders as one of the main forms of causal inference.
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?