arXiv:2607. 23439v1 Announce Type: cross Abstract: Graphical models are ubiquitous in social and empirical science as they are intuitive and easy to use.
arXiv:2608.24602v1 Announce Type: cross Abstract: Probabilistic models of Directed Acyclic Graphs (DAGs) with latent variables impose equality constraints on the observed data distribution beyond ord...
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.
The paper investigates identifiability in linear parametric models where variables follow either an ordered logit or a one‑parameter exponential family distribution. It proves that the direction of every edge linking an ordinal node to an exponential‑family node can be determined from the joint distribution, provided each node has at least three categories or support points, respectively. Numerical experiments confirm that these orientations can be distinguished even when conditional independence tests cannot separate them.
arXiv:2505. 03649v4 Announce Type: replace-cross Abstract: Modeling of intricate relational patterns has become a cornerstone of contemporary statistical research and related data science fields.
The paper develops a nonasymptotic theoretical framework for estimating hyperparameters in hierarchical Bayesian models applied to large, inhomogeneous complex network dynamical systems. It provides bounds on the deviation of hyperparameter estimates as network size grows, first for independent nodes and then extending to weakly‑dependent nodes, and validates these results with numerical experiments on SIS and spiking neuronal network models.
The paper introduces a model‑agnostic inference framework for partially identified causal effects that leverages covariate information without requiring discrete covariates or accurate conditional distribution estimates. Using duality theory for optimal transport, the method delivers uniformly valid inference in randomized experiments, is doubly robust in observational settings, achieves asymptotic unbiasedness when nuisance parameters converge semiparametrically, and allows multiplier‑bootstrap selection of covariates and models while remaining computationally efficient. Empirical applications show the approach consistently narrows identified sets and confidence intervals without imposing extra structural assumptions.
arXiv:2404.17763v3 Announce Type: replace-cross Abstract: Probabilistic graphical models that encode an underlying Markov random field are fundamental building blocks of generative modeling to learn...
arXiv:2608. 10470v1 Announce Type: new Abstract: Fair representation learning with a continuous sensitive attribute $S$ requires a representation $Z$ that is statistically independent of $S$.
arXiv:2607.04743v3 Announce Type: replace-cross Abstract: Higher-order influence functions, introduced in a series of articles (Robins et al., 2008, 2009a; van der Vaart, 2014; Robins et al., 2016, 2...
arXiv:2511. 14441v2 Announce Type: replace-cross Abstract: To distinguish Markov equivalent graphs in causal discovery, it is necessary to restrict the structural causal model.
arXiv:2607. 20502v1 Announce Type: new Abstract: To allow for principled comparison between two probabilistic graphical models defined over non-identical variable sets, they have to be lifted to a common measurable space.
The paper introduces JSP-GFN, a Generative Flow Network that jointly infers the structure and parameters of a Bayesian Network. It sequentially generates a directed acyclic graph edge by edge and then samples the corresponding conditional probability parameters once the full structure is known. Experiments on simulated and real data show that JSP‑GFN accurately approximates the joint posterior and outperforms existing methods.