Counterfactual Identifiability of Bijective Causal Models
Abstract
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.
Cite
Text
Nasr-Esfahany et al. "Counterfactual Identifiability of Bijective Causal Models." International Conference on Machine Learning, 2023.Markdown
[Nasr-Esfahany et al. "Counterfactual Identifiability of Bijective Causal Models." International Conference on Machine Learning, 2023.](https://mlanthology.org/icml/2023/nasresfahany2023icml-counterfactual/)BibTeX
@inproceedings{nasresfahany2023icml-counterfactual,
title = {{Counterfactual Identifiability of Bijective Causal Models}},
author = {Nasr-Esfahany, Arash and Alizadeh, Mohammad and Shah, Devavrat},
booktitle = {International Conference on Machine Learning},
year = {2023},
pages = {25733-25754},
volume = {202},
url = {https://mlanthology.org/icml/2023/nasresfahany2023icml-counterfactual/}
}