aiqbench analysis
ADIGen addresses critical limitations in counterfactual generation by integrating causal invariance and orthogonal statistical learning to ensure robustness against distribution shifts and model misspecification. This framework enables reliable decision-making under complex, high-dimensional interventions without relying on unstable density-ratio estimations or direct effect decompositions. The advancement represents a significant step forward in building generative models capable of accurate world modeling across diverse environments.
Automatic, Debiased, and Invariant Counterfactual Generation under General Interventions
arXiv:2606.07399v2 Announce Type: replace-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 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. We then extend this framework to multiple, interacting objects with a joint intervention, and apply ADIGen to counterfactual world modeling. In contrast to standard statistical settings, the joint outcome is modeled natively without the need for exposure mappings or direct/indirect effect decompositions.
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. We then extend this framework to multiple, interacting objects with a joint intervention, and apply ADIGen to counterfactual world modeling. In contrast to standard statistical settings, the joint outcome is modeled natively without the need for exposure mappings or direct/indirect effect decompositions.
Machine Learning (stat.ML); Machine Learning (cs.LG)
arXiv:2606.07399 [stat.ML]
(or arXiv:2606.07399v2 [stat.ML] for this version)
https://doi.org/10.48550/arXiv.2606.07399 Focus to learn more arXiv-issued DOI via DataCite
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