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Found 7 papers, 4 papers with code
Integer Programming for Learning Directed Acyclic Graphs from Non-identifiable Gaussian Models
We study the problem of learning directed acyclic graphs from continuous observational data, generated according to a linear Gaussian structural equation model.
Extremal graphical modeling with latent variables
Extremal graphical models encode the conditional independence structure of multivariate extremes and provide a powerful tool for quantifying the risk of rare events.
Causality-oriented robustness: exploiting general additive interventions
In a linear setting, we prove that DRIG yields predictions that are robust among a data-dependent class of distribution shifts.
Characterization and Greedy Learning of Gaussian Structural Causal Models under Unknown Interventions
We leverage this procedure and evaluate the performance of GnIES on synthetic, real, and semi-synthetic data sets.
Provable concept learning for interpretable predictions using variational autoencoders
In safety-critical applications, practitioners are reluctant to trust neural networks when no interpretable explanations are available.
A Fast Non-parametric Approach for Local Causal Structure Learning
DAG-FOCI outputs the set of parents of a response variable of interest.
Learning Exponential Family Graphical Models with Latent Variables using Regularized Conditional Likelihood
Fitting a graphical model to a collection of random variables given sample observations is a challenging task if the observed variables are influenced by latent variables, which can induce significant confounding statistical dependencies among the observed variables.
