  We propose a Goodness of Causal Fit (GCF) measure which depends on Judea
Pearl's ``do" interventions. This is different from Goodness of Fit (GF)
measures, which do not use interventions. Given a set ${\cal G}$ of DAGs with
the same nodes, to find a good $G\in {\cal G}$, we propose plotting $GCF(G)$
versus $GF(G)$ for all $G\in {\cal G}$, and finding a graph $G\in {\cal G}$
with a large amount of both types of goodness.
