AINeutralarXiv – CS AI · Feb 274/105
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Causal Direction from Convergence Time: Faster Training in the True Causal Direction
Researchers introduce Causal Computational Asymmetry (CCA), a new method for identifying causal relationships by training neural networks in both directions and determining causality based on which direction converges faster during optimization. The method achieved 26/30 correct causal identifications across synthetic benchmarks and is embedded in a broader Causal Compression Learning framework.