arXiv:2607. 05153v1 Announce Type: cross Abstract: Scientists often seek to draw causal inferences from structured data that is not independently and identically distributed, such as spatial data, network data, or molecular data.
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:2405. 19062v2 Announce Type: replace-cross Abstract: Continuous-Time Dynamic Graphs (CTDGs) enable fine-grained modeling of evolving relational systems.
arXiv:2505. 19809v3 Announce Type: replace-cross Abstract: In many real-world applications of regression, conditional probability estimation, and uncertainty quantification, exploiting symmetries rooted in physics or geometry can dramatically improve generalization and sample efficiency.
arXiv:2607. 08238v1 Announce Type: new Abstract: Recent algorithmic advances have made directed acyclic graph (DAG) structure learning scalable for causal discovery.
arXiv:2606. 06440v1 Announce Type: new Abstract: Data-driven causal relationship identification is pertinent to advancing understanding of complex systems both within and beyond science.
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:2610.00968v1 Announce Type: cross Abstract: Causal representation learning aims to discover robust features by exploiting the causal structure underlying data generation. Existing methods requi...
arXiv:2607. 09645v1 Announce Type: cross Abstract: Many real-world processes can be represented as compositions of functions along a directed acyclic graph (DAG).
arXiv:2608. 04930v1 Announce Type: cross Abstract: Bayesian causal discovery seeks to determine the posterior distribution of causal theories, which are interpreted as directed acyclic graphs (DAGs) that explain the observed data.
RW-Flow presents a new one‑step generative framework for data on compact manifolds, leveraging Wasserstein gradient flows. The authors derive a necessary and sufficient identifiability condition for velocity fields on compact, connected Riemannian manifolds, showing that a symmetric, Lipschitz‑continuous cost function yields identifiability iff its Gibbs kernel is nondegenerate. Experiments on geospatial events, protein and RNA torsion angles, and discretized manifolds demonstrate that RW‑Flow surpasses existing one‑step methods across most benchmark settings.
arXiv:2608.31045v1 Announce Type: new Abstract: Rotational symmetry is one of the most important structural principles in machine learning on 3D data. In applications ranging from physics and materia...
arXiv:2604. 22416v2 Announce Type: replace-cross Abstract: Latent variables pose a fundamental obstacle to both causal discovery and inference.