It is useful to realize that Markov condition is quite prevalent in our thinking about the world. We can derive it from a simple assumption that all interactions in a system that we model are deterministic. Any definition of relevance is bound to include conditioning, as things may become relevant or cease to be relevant given new observations. I believe that I am here in accord with the author. The task of prediction, for example, addressed by Goldszmidt's paper, requires that the model contain causal information.
Causality is also useful in interacting with humans, as it plays an important part in our reasoning.
I believe that causality lies at the foundation of the notion of relevance. Mechanisms determine how variables in a model will be connected and how they will interact. This provides a basis for constructing models and for reasoning about relevance. Constructing a model of a real world system amounts to capturing formally the mechanisms acting in that system. The most likely place to find variables that are relevant for a given variable are the causal mechanisms in which this variable participates.