Treating Causality as a Reading Rule — Repositioning Causality from "Cause" to Model Optimization —
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2025
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| _version_ | 1866901133143834624 |
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| author | 長嶺, 智 |
| author_facet | 長嶺, 智 |
| contents | <p>This paper repositions causality not as a primitive causal force or substance, but as a “reading rule” adopted to interpret established histories consistently.</p> <p>No new causal theory, model, entity, or equation is introduced. Existing frameworks in causal inference, optimization, and machine learning are preserved. By fixing the practical role causality actually plays, this paper clarifies how excessive causal assumptions can arise in regions where description is not yet established, and how limiting causality to a reading rule can improve the stability and honesty of model design.</p> <p>This stance is consistent with the Zero-Matter Universal Theory (Z-MUT), while the paper is written to stand independently as a self-contained conceptual discussion.</p> <p>English text is provided via automatic translation.<br>For authoritative meaning and nuance, please refer to the Japanese original.</p> <p>This work is related to the Zero-Matter Universal Theory (Z-MUT), as outlined in the following publication:<br>DOI: 10.5281/zenodo.17962809</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_17979083 |
| institution | Zenodo |
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| publishDate | 2025 |
| publisher | Zenodo |
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| spellingShingle | Treating Causality as a Reading Rule — Repositioning Causality from "Cause" to Model Optimization — 長嶺, 智 causality causal inference reading rule modeling optimization machine learning reinforcement learning credit assignment causal graph structural equation model Z-MUT <p>This paper repositions causality not as a primitive causal force or substance, but as a “reading rule” adopted to interpret established histories consistently.</p> <p>No new causal theory, model, entity, or equation is introduced. Existing frameworks in causal inference, optimization, and machine learning are preserved. By fixing the practical role causality actually plays, this paper clarifies how excessive causal assumptions can arise in regions where description is not yet established, and how limiting causality to a reading rule can improve the stability and honesty of model design.</p> <p>This stance is consistent with the Zero-Matter Universal Theory (Z-MUT), while the paper is written to stand independently as a self-contained conceptual discussion.</p> <p>English text is provided via automatic translation.<br>For authoritative meaning and nuance, please refer to the Japanese original.</p> <p>This work is related to the Zero-Matter Universal Theory (Z-MUT), as outlined in the following publication:<br>DOI: 10.5281/zenodo.17962809</p> |
| title | Treating Causality as a Reading Rule — Repositioning Causality from "Cause" to Model Optimization — |
| topic | causality causal inference reading rule modeling optimization machine learning reinforcement learning credit assignment causal graph structural equation model Z-MUT |
| url | https://doi.org/10.5281/zenodo.17979083 |