AI RESEARCH
Identification of Bivariate Causal Directionality Based on Anticipated Asymmetric Geometries
arXiv CS.LG
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ArXi:2603.26024v1 Announce Type: new 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. The AAG method compares the actual conditional distributions to anticipated ones along two variables.