  We outline a representation for discrete multivariate distributions in terms
of interventional potential functions that are globally normalized. This
representation can be used to model the effects of interventions, and the
independence properties encoded in this model can be represented as a directed
graph that allows cycles. In addition to discussing inference and sampling with
this representation, we give an exponential family parametrization that allows
parameter estimation to be stated as a convex optimization problem; we also
give a convex relaxation of the task of simultaneous parameter and structure
learning using group l1-regularization. The model is evaluated on simulated
data and intracellular flow cytometry data.
