What I want to know is: why is it necessary to come up with ad-hoc solutions for these types of problems instead of using bayesian networks, which were invented precisely for this purpose (and have been wildly successful at it)?

It's not necessary. The sort of reasoning Steve is doing here looks to me like putting informative prior distributions on the causal effect parameters. If we run pcalg on the dataset and it tells us "nut consumption causes age," we'll probably say "well, something went wrong." But with a large study size like this, it's not clear to me that informative priors are going to push the final estimate around by much. Hopefully, the DAG discovery methods will discover common causes, but the priors may be most useful in establishing correlations between the parameters- "if nuts don't help with X, we don't expect they help with Y, because of an underlying similarity between X and Y"- but I don't know if that's better accomplished by just adding another node to the system.

My addition was eyeballing what the DAG results would look like, which is nowhere near as good as getting the data and running pcalg on it.