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Found 6 papers, 3 papers with code
Causal Discovery of Linear Non-Gaussian Causal Models with Unobserved Confounding
In this setting, the causal structure is identifiable, but, in general, it is not possible to identify the specific causal effects.
Log-concave density estimation in undirected graphical models
We study the problem of maximum likelihood estimation of densities that are log-concave and lie in the graphical model corresponding to a given undirected graph $G$.
Ultra-marginal Feature Importance: Learning from Data with Causal Guarantees
Scientists frequently prioritize learning from data rather than training the best possible model; however, research in machine learning often prioritizes the latter.
Learning Linear Non-Gaussian Graphical Models with Multidirected Edges
In this paper we propose a new method to learn the underlying acyclic mixed graph of a linear non-Gaussian structural equation model given observational data.
Estimation of Monge Matrices
Monge matrices and their permuted versions known as pre-Monge matrices naturally appear in many domains across science and engineering.
Duality of Graphical Models and Tensor Networks
For example, marginalization in a graphical model is dual to contraction in the tensor network.
