Counterfactual image editing is an important task in generative AI, which
asks how an image would look if certain features were different. The current
literature on the topic focuses primarily on changing individual features while
remaining silent about the causal relationships between these features, as
present in the real world. In this paper, we formalize the counterfactual image
editing task using formal language, modeling the causal relationships between
latent generative factors and images through a special type of model called
augmented structural causal models (ASCMs). Second, we show two fundamental
impossibility results: (1) counterfactual editing is impossible from i.i.d.
image samples and their corresponding labels alone; (2) even when the causal
relationships between the latent generative factors and images are available,
no guarantees regarding the output of the model can be provided. Third, we
propose a relaxation for this challenging problem by approximating
non-identifiable counterfactual distributions with a new family of
counterfactual-consistent estimators. This family exhibits the desirable
property of preserving features that the user cares about across both factual
and counterfactual worlds. Finally, we develop an efficient algorithm to
generate counterfactual images by leveraging neural causal models.

反事实图像编辑是生成式人工智能中的重要任务，本文提出了一种利用增强的结构因果模型来建模潜在生成因子与图像之间的因果关系，并通过一种新的家族反事实一致估计器对非可识别的反事实分布进行近似，以解决此挑战性问题。