A novel approach to the relationships between data features -- based on comprehensive examination of mathematical, technological, and causal methodology
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866910840521752576 |
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| author | Kim, JaeHong |
| author_facet | Kim, JaeHong |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2502_15838 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A novel approach to the relationships between data features -- based on comprehensive examination of mathematical, technological, and causal methodology Kim, JaeHong Artificial Intelligence Machine Learning 68T27 (Primary) 00A30, 03A05 (Secondary) I.2.3; F.4.1 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. |
| title | A novel approach to the relationships between data features -- based on comprehensive examination of mathematical, technological, and causal methodology |
| topic | Artificial Intelligence Machine Learning 68T27 (Primary) 00A30, 03A05 (Secondary) I.2.3; F.4.1 |
| url | https://arxiv.org/abs/2502.15838 |