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 First implementation of a geometric causal inference model
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 computational model, geomtric causal inference, 3d geometry, bioinformatics, dag
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Registry info in Safe Creative
Identifier 2510273507253
Entry date Oct 27, 2025, 4:25 PM UTC
License Creative Commons Attribution-NonCommercial 4.0
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Author. Holder david Graupere Villà. Date Oct 27, 2025.
Information available at https://www.safecreative.org/work/2510273507253-first-implementation-of-a-geometric-causal-inference-model
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