Causal Effect Identification in Alternative Acyclic Directed Mixed Graphs

Alternative acyclic directed mixed graphs (ADMGs) are graphs that may allow causal effect identification in scenarios where Pearl’s original ADMGs may not, and vice versa. Therefore, they complement each other. In this paper, we introduce a sound algorithm for identifying arbitrary causal effects from alternative ADMGs. Moreover, we show that the algorithm is complete for identifying the causal effect of a single random variable on the rest. We also show that the algorithm follows from a calculus similar to Pearl’s do-calculus.