arXiv:2606. 18074v1 Announce Type: cross Abstract: Causal discovery seeks to uncover the causal dependencies among variables.
arXiv:2502.20115v4 Announce Type: replace Abstract: Causal discovery is a difficult problem that typically relies on strong assumptions on the data-generating model, such as non-Gaussianity. In pract...
arXiv:2609.23535v1 Announce Type: new Abstract: Causal discovery from observational data is a fundamental yet challenging task in scientific research. While existing approaches are primarily based on...
arXiv:2609.27256v1 Announce Type: cross Abstract: We study causal discovery where each node is a random function. Previous studies on this topic rely on structural assumptions, e.g., linearity or non...
arXiv:2607. 11816v1 Announce Type: new Abstract: Causal discovery algorithms learn a network that describes the causal dependencies among random variables.
arXiv:2606. 19594v1 Announce Type: new Abstract: Causal abstractions formalize when a high-level structural causal model (SCM) captures the interventional behavior of a lower-level SCM.
arXiv:2609. 18535v1 Announce Type: new Abstract: Causal discovery aims to recover causal relationships from observed data.
arXiv:2606. 08196v1 Announce Type: cross Abstract: We study causal discovery from observational data when some variables are hidden and the data-generating process follows a location-scale noise model (LSNM).
arXiv:2607. 11510v1 Announce Type: new Abstract: Causal discovery from observational tabular data remains fundamentally challenging, primarily due to the heterogeneity of underlying causal mechanisms and the high-dimensional combinatorial search space of Directed Acyclic Graphs (DAGs).
arXiv:2609.30643v1 Announce Type: new Abstract: We consider the problem of learning the underlying causal directed acyclic graph (DAG) structure corresponding to a structural equation model (SEM) wit...
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 addresses causal discovery in Directed Acyclic Graphs where nodes are either ordinal (modeled with an ordered logit) or follow a one‑parameter exponential family distribution. It proves that the direction of edges between such nodes is identifiable for generic parameter values, extending prior Ordinal‑Poisson results. The authors also propose a score‑based exhaustive search and a masked continuous optimization method using DAGMA, and demonstrate through simulations that these approaches recover orientations that are otherwise unidentifiable under classical structural equation models.
arXiv:2609.23516v1 Announce Type: new Abstract: Structural causal models for time series recover contemporaneous and lagged effects, but most methods require complete observation windows and become m...