Automatic, Debiased, and Invariant Counterfactual Generation under General Interventions

• URL: http://arxiv.org/abs/2606.07399v1
• Date: Fri, 05 Jun 2026 15:40:59 GMT
• Title: Automatic, Debiased, and Invariant Counterfactual Generation under General Interventions
• Abstract summary: ADIGen is a framework for automatic, debiased, and invariant counterfactual generation under general interventions.<n>We show that ADIGen controls counterfactual risk under general interventions, with a product-bias nuisance and an invariant risk bound across environments.
• Score: 7.1743353314344835
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 outcomes. ADIGen combines Riesz regression to avoid unstable density-ratio estimation, causal invariance to improve generalization under distribution shift, and orthogonal statistical learning to obtain doubly robust guarantees against nuisance model misspecification. We provide excess-risk bounds showing that ADIGen controls counterfactual risk under general interventions, with a product-bias nuisance remainder and an invariant risk bound across environments.

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