This depends on what you mean. When applied to a causal design (e.g., a randomized experiment where there is no post-randomization trickery) causal language is hardly needed at all.
The real problem with causal epidemiology is when the rubber hits the road. Lots of methodologists talk about notation and theory but can’t give us a real complete case study based on real data – a case study in which the DAG is justified by the subject matter and all needed measurements are available in the data. A case study where the rest of us can learn how to do real and not theoretical causal inference. See the call for examples here.