@inproceedings{
yao2023multiview,
title={Multi-View Causal Representation Learning with Partial Observability},
author={Dingling Yao and Danru Xu and Sebastien Lachapelle and Sara Magliacane and Perouz Taslakian and Georg Martius and Julius von K{\"u}gelgen and Francesco Locatello},
booktitle={Causal Representation Learning Workshop at NeurIPS 2023},
year={2023},
url={https://openreview.net/forum?id=E8IhOxNREv}
}
We present a unified framework for studying the identifiability of representations learned from simultaneously observed views, such as different data modalities. We allow a partially observed setting in which each view constitutes a nonlinear mixture of a subset of underlying latent variables, which can be causally related. We prove that the information shared across all subsets of any number of views can be learned up to a smooth bijection using contrastive learning and a single encoder per view. We also provide graphical criteria indicating which latent variables can be identified through a simple set of rules, which we refer to as identifiability algebra. Our general framework and theoretical results unify and extend several previous works on multi-view nonlinear ICA, disentanglement, and causal representation learning. We experimentally validate our claims on numerical, image, and multi-modal data sets. Further, we demonstrate that the performance of prior methods is recovered in different special cases of our setup. Overall, we find that access to multiple partial views enables identifying a more fine-grained representation, under the generally milder assumption of partial observability.
Multi-View Causal Representation Learning with Partial Observability
Dingling Yao Danru Xu Sebastien Lachapelle Sara Magliacane Perouz Taslakian Georg Martius Julius von Kügelgen Francesco Locatello