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Generative models can fail to produce globally consistent counterfactuals when causal graphs have complex topologies, but a novel sheaf-theoretic framework with entropic regularization can overcome these limitations.
Deterministic causal models can't handle extreme counterfactual interventions without ripping apart, unless you use topology-aware methods.