Goodness of Causal Fit
This addresses the challenge of causal model selection for researchers in causal inference, but appears incremental as it builds on existing GF measures by incorporating interventions.
The paper tackles the problem of selecting a good Directed Acyclic Graph (DAG) from a set with the same nodes by proposing a Goodness of Causal Fit (GCF) measure based on Judea Pearl's 'do' interventions, and suggests plotting GCF against Goodness of Fit (GF) to find a graph with high values in both measures.
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.