Topological Ignorability for Structural Causal Effects Beyond Means

ArXi:2606.01184v1 Announce Type: cross Many interventions alter the structure of an outcome distribution rather than its mean: they can split a population into disconnected regimes, create loops or holes, generate branches, or reorganize an outcome cloud while leaving the average response nearly unchanged. In such settings, mean-based causal estimands such as the average treatment effect may miss important structural effects. We define a covariate-standardized topological-geometrical causal effect and develop practical estimators.