arXiv:2608.28991v1 Announce Type: cross
Abstract: Causal representation learning (CRL) aims to recover latent causal variables and their structural relations from high-dimensional observations. Exist...
By Haijie Xu, Chen Zhang
arXiv:2509. 01916v2 Announce Type: replace Abstract: Causal disentanglement from soft interventions is identifiable under the assumptions of linear interventional faithfulness and availability of both observational and interventional data.
By Jifan Zhang, Michelle M. Li, Elena Zheleva
arXiv:2608. 16245v1 Announce Type: new Abstract: Disentangled representation learning seeks latent representations whose indicidual dimensions each align with a distinct covariate.
By Ma{\l}gorzata {\L}az\k{e}cka, Ewa Szczurek
arXiv:2606. 17516v1 Announce Type: cross Abstract: Causal discovery from observational data remains challenging due to the need to recover directed structure and latent confounding without interventions.
By Patrick Bl\"obaum, Krishnakumar Balasubramanian, Shiva Prasad Kasiviswanathan
Neuro-Causal Factor Analysis (NCFA) reimagines traditional factor analysis by integrating causal structure learning and deep generative modeling. The method learns a directed graph linking latent and observed variables, then trains a deep generative model that respects the graph’s Markov factorization. Experiments on synthetic and real datasets show NCFA achieves lower reconstruction error than standard FA and better latent distribution recovery than a variational autoencoder, while offering a sparser architecture, reduced complexity, and causal interpretability.
By Alex Markham, Mingyu Liu, Bryon Aragam, Liam Solus
arXiv:2606. 21806v2 Announce Type: replace Abstract: Deep generative models reproduce the observational distribution of their training data, inheriting any spurious associations it contains.
By Jingyuan Chen, Kangrui Ruan, Junzhe Zhang