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

TL;DR

This paper introduces the Convergent Fusion Paradigm (CFP) theory, a comprehensive framework combining mathematical, technological, and causal methods to better analyze feature relationships in AI, enhancing transparency and interpretability.

Contribution

The paper proposes the CFP theory, integrating Hilbert space, backward causation, and Riemannian geometry to improve causal modeling and feature relationship analysis in AI.

Findings

CFP theory offers a more precise analysis of feature interactions.

It addresses the common cause problem in causal inference.

The framework enhances AI transparency and interpretability.

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.