A novel approach to the relationships between data features -- based on comprehensive examination of mathematical, technological, and causal methodology

• URL: http://arxiv.org/abs/2502.15838v1
• Date: Thu, 20 Feb 2025 14:36:37 GMT
• Title: A novel approach to the relationships between data features -- based on comprehensive examination of mathematical, technological, and causal methodology
• Authors: JaeHong Kim,
• Abstract summary: Current mathematical, technological, and causal methodologies rely on externalization techniques that normalize feature relationships.<n>This study proposes the Convergent Fusion Paradigm theory, a framework integrating mathematical, technological, and causal perspectives.
• Score: 0.3496513130883643
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: The expansion of artificial intelligence (AI) has raised concerns about transparency, accountability, and interpretability, with counterfactual reasoning emerging as a key approach to addressing these issues. However, current mathematical, technological, and causal methodologies rely on externalization techniques that normalize feature relationships within a single coordinate space, often distorting intrinsic interactions. This study proposes the Convergent Fusion Paradigm (CFP) theory, a framework integrating mathematical, technological, and causal perspectives to provide a more precise and comprehensive analysis of feature relationships. CFP theory introduces Hilbert space and backward causation to reinterpret the feature relationships as emergent structures, offering a potential solution to the common cause problem -- a fundamental challenge in causal modeling. From a mathematical -- technical perspective, it utilizes a Riemannian manifold-based framework, thereby improving the structural representation of high- and low-dimensional data interactions. From a causal inference perspective, CFP theory adopts abduction as a methodological foundation, employing Hilbert space for a dynamic causal reasoning approach, where causal relationships are inferred abductively, and feature relationships evolve as emergent properties. Ultimately, CFP theory introduces a novel AI modeling methodology that integrates Hilbert space, backward causation, and Riemannian geometry, strengthening AI governance and transparency in counterfactual reasoning.

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