Figure 2. Comparison of DDGraphs and DAGs.

(A) The causal neighbourhood of the target variable T consists of variables X1 and X2, while T's Markov blanket consists of X1, X2, X4 (in ovals). The remaining variables X3 and X5 have indirect dependence (in rectangles). The DDGraph (left) and the DAG (right) represent the same conditional dependencies. The causal neighbourhood/the Markov blanket and the variable in indirect dependence are distinguishable by the variable shapes in the DDGraph, but have to be inferred in the DAG by following the edges. (B) joint dependency patterns representable in the DDGraph (left) cannot be represented by DAGs (right). The DAG shown here represents the conditional independencies between X1 (or X2) and T given X2 (or X1), but it does not represent the marginal dependency between X1 (or X2) and T. Neither this DAG or any other DAG can represent the entire joint dependency pattern.