The Global Markov Property for a Mixture of DAGs

Understand

Real causal processes may contain feedback loops and change over time.

• In this paper, we model cycles and non-stationary distributions using a mixture of directed acyclic graphs (DAGs).
• We then study the conditional independence (CI) relations induced by a density that factorizes according to a mixture of DAGs in two steps.
• First, we generalize d-separation for a single DAG to mixture d-separation for a mixture of DAGs.