提出ADIGen框架，实现复杂干预下的稳定、无偏、泛化强反事实生成。
Automatic, Debiased, and Invariant Counterfactual Generation under General Interventions
- 用瑞斯回归避免密度比估计不稳，结合因果不变性提升跨环境泛化。
- 理论证明反事实风险受控，且在多环境间保持不变，误差项为偏差乘积。
- 支持高维联合干预，无需暴露映射或效应分解，适合复杂系统建模。
反事实生成模型在复杂干预决策中潜力巨大，但现有方法受限于估计不稳定、跨环境泛化差以及干扰模型误设带来的偏差。本文提出ADIGen框架，实现一般干预下自动、无偏、不变的反事实生成，涵盖高维干预与结果。ADIGen结合瑞斯回归以规避不稳定的密度比估计，利用因果不变性提升分布转移下的泛化能力，并通过正交统计学习获得对干扰模型误设的双重稳健性保证。理论分析给出超额风险界，表明ADIGen在一般干预下控制反事实风险，其剩余项为偏差乘积，且风险在不同环境间保持不变。进一步将框架扩展至多个相互作用对象的联合干预，并应用于反事实世界建模。不同于标准统计设定，联合结果被原生建模，无需暴露映射或直接/间接效应分解。
原文摘要 · Abstract (English)
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.
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