Goodness of Causal Fit
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.
- Publication:
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arXiv e-prints
- Pub Date:
- May 2021
- DOI:
- 10.48550/arXiv.2105.02172
- arXiv:
- arXiv:2105.02172
- Bibcode:
- 2021arXiv210502172T
- Keywords:
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- Statistics - Methodology;
- Computer Science - Artificial Intelligence
- E-Print:
- This second version has major changes. Most notably, it introduces what we call the hospitality of a node and defines GCF in terms of hospitalities