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.

翻译：反事实结果的生成模型在支持复杂干预下的决策方面具有巨大潜力，但现有方法受限于不稳定的估计、跨环境泛化能力差以及因干扰模型误设导致的偏差。我们提出ADIGen框架，该框架支持通用干预（包括高维干预与结果）下的自动、去偏与不变反事实生成。ADIGen结合了Riesz回归（以避免不稳定的密度比估计）、因果不变性（以提升分布偏移下的泛化能力）以及正交统计学习（以获得针对干扰模型误设的双重稳健保障）。我们给出了超额风险界，证明ADIGen能够控制通用干预下的反事实风险，并具备乘积偏差干扰余项以及跨环境不变风险界。