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to identify unoriented edges that denote the existence of unconfounded causal relations and then applying causal orientation techniques to orient unoriented edges.
The latter approach can sometimes improve accuracy of the causal orientation method while reducing the fraction of causally oriented edges [ 17].
As an enhancement to using individual causal orientation techniques, we introduce ensemble causal orientation models that combine decisions of all available individual causal orientation methods in order to produce a more powerful predictor of causal orientation.
We also introduced a novel ensemble technique for causal orientation that combines decisions of individual causal orientation methods.
As can be seen, ensemble causal orientation achieves higher values of AUC than any individual causal orientation method in all four gold standards.
Second, we described a novel ensemble technique for causal orientation that combines decisions of individual causal orientation methods to provide a more powerful predictor of causal directionality.
The use of ensemble modelling for causal orientation is motivated by our empirical observations that there is no single causal orientation methodology that performs perfectly (i.e., with 1.0 accuracy or AUC), many causal orientation methods appear to perform different than chance, and causal orientation methods often make errors in orienting different edges.
Bold p-values indicate a statistically significant performance improvement by using an ensemble causal orientation.
Two metrics were used to assess performance of causal orientation algorithms.
Neither of these results improves the best individual causal orientation method in the respective gold standard.
The ensemble method was found to be more accurate than any individual causal orientation method.
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