J. Mooij

Causal Reasoning with Bipartite Graphical Causal Models

This work proposes bipartite graphical causal models (BGCMs), in which the structure of a system of equations is encoded by a bipartite graph with variable and equation nodes, and forms a Markov property in terms of a new graphical separation criterion (B-separation) that exploits the functional determinism inherent in the equations.