Goodness of Causal Fit

TL;DR

This paper introduces a new measure called Goodness of Causal Fit (GCF) that utilizes Judea Pearl's do-interventions to evaluate causal models, providing a novel approach to assess causal fit compared to traditional goodness-of-fit measures.

Contribution

The paper proposes the GCF measure based on interventions, offering a new method to evaluate and select causal graphs from a set of DAGs.

Findings

GCF depends on do-interventions, unlike traditional measures.

Plotting GCF versus GF helps identify well-fitting causal graphs.

The method enables better causal model selection.

Abstract

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.