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.
arXiv:2606. 28854v1 Announce Type: cross Abstract: The common factor analytic model is related to Helmholtz and Boltzmann machines, can be conceived as a linear autoencoder, or can be thought of as a single-hidden-layer generative neural network.
The paper introduces a variational inference framework that jointly discovers latent clusters of variables and the causal relationships among those clusters. It models clusters with categorical distributions and graph structures with Bernoulli distributions, deriving variational lower bounds and estimation techniques for learning both cluster assignments and causal links. The method’s effectiveness is shown on synthetic and real datasets.
arXiv:2606. 13024v1 Announce Type: cross Abstract: Granger Causal Discovery (GCD) is fundamental for analyzing temporal dependencies in complex systems.
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.
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.
arXiv:2401. 04890v2 Announce Type: replace-cross Abstract: This work introduces a novel principle for disentanglement we call mechanism sparsity regularization, which applies when the latent factors of interest depend sparsely on observed auxiliary variables and/or past latent factors.
arXiv:2606. 30648v1 Announce Type: cross Abstract: Causal mediation analysis decomposes a treatment effect into indirect pathways through mediators and direct pathways not operating through them.
arXiv:2607. 22262v1 Announce Type: cross Abstract: Modeling shared and subject-specific structure in multisubject spatiotemporal data remains challenging, particularly in neuroimaging, where both spatial and temporal patterns exhibit rich variability across subjects.
arXiv:2607. 08254v1 Announce Type: new Abstract: Quantifying variability in a target population relative to a reference population is central to many scientific and clinical problems (e.
arXiv:2407. 21740v3 Announce Type: replace-cross Abstract: Factor analysis, often regarded as a Bayesian variant of matrix factorization, offers superior capabilities in capturing uncertainty, modeling complex dependencies, and ensuring robustness.
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...
arXiv:2607. 18883v1 Announce Type: cross Abstract: A central aim of unsupervised learning is to uncover latent factors that explain dependencies among observations.