Identification of Bivariate Causal Directionality Based on Anticipated Asymmetric Geometries
Identification of causal directionality in bivariate numerical data is a fundamental research problem with important practical implications. This paper presents two alternative methods to identify direction of causation by considering conditional distributions: (1) Anticipated Asymmetric Geometries (AAG) and (2) Monotonicity Index (MI). The AAG method compares the actual conditional distributions to anticipated ones along two variables. Different comparison metrics, such as Pearson correlation, cosine distance, Hellinger distance, Jaccard index, Jeffreys divergence, K-L divergence, K-S distance, MAE, MSE, mutual information, and Wasserstein distance have been evaluated. Anticipated distri...
| Authors | Alex Glushkovsky |
|---|---|
| Primary category | cs.LG |
| Published | 27 March 2026 |
| Last updated at source | 8 October 2026 |
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