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Check to add information theory metrics, check for recent Bollen and Lin paper for inspiration
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Run with larger sample sizes, increased model complexity ... make it bigger
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Derive expectations of metric variations, like: "the Fisher C statistics actually compiles all the p-values of d-sep test in an omnibus statistical test. D-sep tests assess whether links that are not included significantly improve the corresponding linear model fit when included. Consequently, it is immediate that too complex models where all the significant links have been included will have no missing link with significant effect, and thus all the D-sep tests will be non-significant"
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