A logical characterization of constraint-based causal discovery

We present a novel approach to constraint-based causal discovery, that takes the form of straightforward logical inference, applied to a list of simple, logical statements about causal relations that are derived directly from observed (in)dependencies. It is both sound and complete, in the sense that all invari-ant features of the corresponding partial an-cestral graph (PAG) are identified, even in the presence of latent variables and selection bias. The approach shows that every identifi-able causal relation corresponds to one of just two fundamental forms. More importantly, as the basic building blocks of the method do not rely on the detailed (graphical) struc-ture of the corresponding PAG, it opens up a range of new opportunities, including more robust inference, detailed accountability, and application to large models.