Invariant Counterfactual Generation under General Interventions
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Automatic, Debiased, and Invariant Counterfactual Generation under General Interventions
arXiv:2606.07399v1 Announce Type: cross Abstract: Generative models for counterfactual outcomes have great potential to support decision-making under complex interventions, but existing approaches are limited by unstable estimation, poor generalization across environments, and bias from nuisance model misspecification. We introduce ADIGen, a framework for automatic, debiased, and invariant counterfactual generation under general interventions, including high-dimensional interventions and...