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.