Ryan Welch

Identifiability Guarantees for Causal Disentanglement from Purely Observational Data

2024 · arXiv (Cornell University)

Causal disentanglement aims to learn about latent causal factors behind data, holding the promise to augment existing representation learning methods in terms of interpretability and extrapolation. Recent advances establish identifiability results assuming that interventions on …