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.
For one it lets you avoid controlling for the wrong variables and causing e.g. spurious correlations by doing so. In fact this is one of the best examples of why a causal model is necessary, because without one you can easily end up with a correlation that doesn’t exist as is illustrated quite nicely in his book.
Do you mean that the new techniques will (1) help you prove which variables should not be controlled for, or that they will (2) help you more clearly describe your causal assumptions, so that you can more easily recognize which variables should not be controlled for according to your assumptions?
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.
