  We consider the problem of learning a causal graph over a set of variables
with interventions. We study the cost-optimal causal graph learning problem:
For a given skeleton (undirected version of the causal graph), design the set
of interventions with minimum total cost, that can uniquely identify any causal
graph with the given skeleton. We show that this problem is solvable in
polynomial time. Later, we consider the case when the number of interventions
is limited. For this case, we provide polynomial time algorithms when the
skeleton is a tree or a clique tree. For a general chordal skeleton, we develop
an efficient greedy algorithm, which can be improved when the causal graph
skeleton is an interval graph.
