DeCaFlow: A Deconfounding Causal Generative Model
Published in ArXiv Preprint, 2025
A practical Causal Generative Model that allows for estimating interventional distributions and counterfactuals,¡ even with unobserved confounders are present. DeCaFlow leverages variational inference and Causal Normalizing Flows to model a Confounded Structural Causal Models, that yields in unbiased estimations of causal queries that are identifiable through adjustment and Proximal Inference.
Cite as:
@article{almodovar2025decaflow,
title={DeCaFlow: A Deconfounding Causal Generative Model},
author={Almod{\'o}var, Alejandro and Javaloy, Adri{\'a}n and Parras, Juan and Zazo, Santiago and Valera, Isabel},
journal={arXiv preprint arXiv:2503.15114},
year={2025}
}
}