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.
@article{arxiv.2302.02228,
title = {Counterfactual Identifiability of Bijective Causal Models},
author = {Arash Nasr-Esfahany and Mohammad Alizadeh and Devavrat Shah},
journal= {arXiv preprint arXiv:2302.02228},
year = {2023}
}