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• The Anticipated Asymmetric Geometries (AAG) method compares actual conditional distributions to anticipated ones along two variables.method 95%
• The Monotonicity Index approach compares the calculated monotonicity indexes of the gradients of conditional distributions along two axes.method 95%
• The tuned AAG method achieves a top accuracy of 77.9% in identifying causal directionality.result 90%
• The AAG method outperforms the Monotonicity Index in identifying causal directionality.result 90%
• Both AAG and Monotonicity Index methods are sensitive to hyperparameters, which impact their accuracy.limitation 90%
• A full factorial Design of Experiment was utilized to tune hyperparameters for the AAG and Monotonicity Index methods.method 95%
• Mutual information and Kendall R2 are important features for screening out pairs that are likely to be misclassified by the AAG method.technical 85%

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