  Causal processes in biomedicine may contain cycles, evolve over time or
differ between populations. However, many graphical models cannot accommodate
these conditions. We propose to model causation using a mixture of directed
cyclic graphs (DAGs), where the joint distribution in a population follows a
DAG at any single point in time but potentially different DAGs across time. We
also introduce an algorithm called Causal Inference over Mixtures that uses
longitudinal data to infer a graph summarizing the causal relations generated
from a mixture of DAGs. Experiments demonstrate improved performance compared
to prior approaches.
