Y_{j,\cdot} is a collection of random variables
That is not the same as there being Y-nodes. Nodes would be part of the graph structure, and so be more visible when you look at the graph.
The only difference is whether the Y-values require their own nodes.
I see. Thanks. I was thrown off because he'd already said that he would "overload" the notation for random variables, using it also to represent nodes or sets of nodes. But what you say makes sense.
I'm not sure what the real difference is, though. The graph is just a way to depict dependencies among random variables. If you're already working with a collection of random variables with given dependencies, the graph is just, well, a graphical way to represent what you're already dealing with. Am I right, then, in thinking that the only differ...
Michael Nielsen has posted a long essay explaining his understanding of the Pearlean causal DAG model. I don't understand more than half, but that's much more than I got out of a few other papers. Strongly recommended for anyone interested in the topic.