Sure, but what does Pearl bring to the table here? The idea of making falsifiable predictions long predates the "Causal Revolution." The warning of "correlation is not causation" still seems as relevant as ever.
If you mean (2), I can't really disagree: explicitly specifying your causal assumptions through a DAG seems like a clarifying step in specifying a model.
If you mean (1), then I must be missing something because I'm not seeing that this set of tools can do that.
My worry is that (2) is mistaken for (1), and that writing down a causal model is conflated with proving that it is true.
If they are already proved, they don't need to be proved further per (1).
If they are not already proved, then they are just assumptions and we are talking about (2).