Counterfactual Identifiability of Bijective Causal Models

• URL: http://arxiv.org/abs/2302.02228v2
• Date: Tue, 6 Jun 2023 21:50:52 GMT
• Title: Counterfactual Identifiability of Bijective Causal Models
• Authors: Arash Nasr-Esfahany, Mohammad Alizadeh, Devavrat Shah
• Abstract summary: We study counterfactual identifiability in causal models with bijective generation mechanisms (BGM) We 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.
• Score: 22.820102235159368
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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