New methods for separating causes from effects in genomics data

BMC Genomics

Table 9 Ensemble causal orientation results and comparison with the best performing individual causal orientation methods

	ECOLI	YEAST	NOTCH1	RELA
Best individual causal orientation method (AUC)	0.828	0.658	0.926	0.970
Ensemble method (AUC)	0.837	0.822	0.984	0.992
Improvement (AUC)	0.009	0.164	0.058	0.022
Statistical significance of improvement (p-value)	0.3407	<0.0001	0.0062	<0.0001

Bold p-values indicate a statistically significant performance improvement by using an ensemble causal orientation. The p-values were obtained from Delong's test for AUC comparison [45].

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