We propose a Goodness of Causal Fit (GCF) measure which depends on Pearl "do" interventions. This is different from a measure of Goodness of Fit (GF), which does not use interventions. Given a DAG set ${\cal G}$, 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.

中文翻译：

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因果拟合优度

我们提出了因果拟合优度（GCF）措施，该措施取决于Pearl的“做”干预措施。这与不使用干预措施的“适合度”（GF）度量不同。给定DAG集合$ {\ cal G} $，以在{\ cal G} $中找到一个好的$ G \，我们建议对所有$ G \ in { \ cal G} $，并在{\ cal G} $中找到具有大量两种类型的善良的图形$ G \。