A Graphical Terminology

Causal structure learning via performing conditional independence tests involves matching conditional independences contained in probability distributions with the conditional independence assumptions encoded in the graph. D-separation (Pearl, 1988) provides a graphical criterion that characterizes the set of conditional independences in the graph. We then say Z d-separates two disjoint subsets of vertices X and Y if it blocks every path from a node in X to a node in Y, and write as X? We refer the readers to (Peters et al., 2017) for more detailed graphical terminology. Here we refer to Causal de Finetti as in its multivariate form, as bivariate is a subcase contained in multivariate form.