"We simplify the causal discovery problem by assuming no confounding, selection bias and feedback."

That's a pretty big caveat.

Not saying the paper is not important, just that it's not (yet?) a full solution to the overall conundrum.

Particularly since the confounding issue is really enormous in science. And sits at the core of the example the article gives in introduction... That would be an achievement in itself to build an experiment without confounder(s).

Causal discovery has been research subject in statistics and machine learning for years.

See, for example:

Causation, Prediction, and Search by Spirtes, Glymour and Scheines (1993) https://www.cs.cmu.edu/afs/cs.cmu.edu/project/learn-43/lib/p...

Computation, Causation, & Discovery (1999), edited by C. Glymour and G. Cooper, MIT Press