Counterfactual Identifiability of Bijective Causal Models
This addresses the challenge of reliable counterfactual inference in causal AI, with applications in domains like video streaming, though it appears incremental as it builds on existing bijective models.
The paper tackles the problem of counterfactual identifiability in causal models with bijective generation mechanisms, establishing identifiability for three common structures with unobserved confounding and proposing a practical learning method that enables efficient counterfactual estimation.
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.