About the work
Causal Geometric Inference is a computational method designed to evaluate causal consistency and geometric coherence within Directed Acyclic Graphs (DAGs).
It integrates 3D regression (via SVD), local geometric testing (localGeomTest()), and information-theoretic coherence metrics.
Developed as part of the methodological exploration of causal structure validation in bioinformatics and systems biology.
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Title Causal Geometric Inference - Version 2
Causal Geometric Inference is a computational method designed to evaluate causal consistency and geometric coherence within Directed Acyclic Graphs (DAGs).
It integrates 3D regression (via SVD), local geometric testing (localGeomTest()), and information-theoretic coherence metrics.
Developed as part of the methodological exploration of causal structure validation in bioinformatics and systems biology.
Work type Research papers, Thesis, Lecture notes
Tags bioinformatics, dag, 3d geometry, geomtric causal inference, computational model
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Registry info in Safe Creative
Identifier 2511033573761
Entry date Nov 3, 2025, 12:33 PM UTC
License Creative Commons Attribution-NonCommercial 4.0
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Author. Holder david Graupere Villà. Date Nov 3, 2025.
Information available at https://www.safecreative.org/work/2511033573761-causal-geometric-inference-version-2
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