This feels difficult for a layman like me. Let's try to clear that up.
> However, as noted by Pearl, interventional queries only form part of a larger hierarchy of causal queries, with counterfactuals sitting at the top.
Regarding "Pearl", paper text cites:
> Tian, J.; and Pearl, J. 2000. Probabilities of causation: Bounds and identification. Annals of Mathematics and Artificial Intelligence, 28(1): 287–313.
which appears to be this: https://link.springer.com/article/10.1023/A:1018912507879
and appears to provide mathematical foundations to answering questions like "did event A cause event B" from observations.
Regarding the "hierarchy", this looks like a clear and very short introduction: http://web.cs.ucla.edu/~kaoru/3-layer-causal-hierarchy.pdf
From the very end of the paper:
> Causal inference is a tool that can have significant impact on society depending on its use. As such the authors are adamant that all uses of causal inference that could have negative societal impacts should be accompanied with the proper due diligence and fail-safes in order to minimize and even eliminate said negative impacts.
Does this translate to "Applications of this research might destroy society, please refrain"?