AINeutralarXiv – CS AI · 7h ago6/10
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Topological Ignorability for Structural Causal Effects Beyond Means
Researchers introduce topological-geometrical causal metrics that capture structural changes in outcome distributions beyond mean-based estimates, proposing 'topological ignorability' as a weaker assumption than standard causal inference methods. The framework identifies cases where traditional average treatment effects miss important distributional shifts, validated through synthetic and real-world benchmarks.