We study counterfactual identifiability in causal models with bijective generation mechanisms (BGM), a class that generalizes several widely-used causal models in the literature. We establish their counterfactual identifiability for three common causal structures with unobserved confounding, and propose a practical learning method that casts learning a BGM as structured generative modeling. Learned BGMs enable efficient counterfactual estimation and can be obtained using a variety of deep conditional generative models. We evaluate our techniques in a visual task and demonstrate its application in a real-world video streaming simulation task.

翻译：我们研究了具有双射生成机制（BGM）的因果模型中的反事实可识别性，该类模型推广了文献中几种广泛使用的因果模型。针对存在未观测混杂变量的三种常见因果结构，我们建立了它们的反事实可识别性，并提出了一种实用学习方法，将学习BGM视为结构化生成建模。学习得到的BGM能够实现高效的反事实估计，并可通过多种深度条件生成模型获得。我们在视觉任务中评估了所提技术，并在真实世界的视频流模拟任务中展示了其应用。