Goodness of Causal Fit

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 $\mathcal{G}$ of DAGs with the same nodes, to find a good $G\in \mathcal{G}$, we propose plotting $GCF(G)$ versus $GF(G)$ for all $G\in \mathcal{G}$, and finding a graph $G\in \mathcal{G}$ with a large amount of both types of goodness.