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Found 16 papers, 5 papers with code
Recovering Latent Confounders from High-dimensional Proxy Variables
Detecting latent confounders from proxy variables is an essential problem in causal effect estimation.
Improving generalisation via anchor multivariate analysis
We introduce a causal regularisation extension to anchor regression (AR) for improved out-of-distribution (OOD) generalisation.
Causal hybrid modeling with double machine learning
Hybrid modeling integrates machine learning with scientific knowledge to enhance interpretability, generalization, and adherence to natural laws.
Discovering Causal Relations and Equations from Data
Discovering equations, laws and principles that are invariant, robust and causal explanations of the world has been fundamental in physical sciences throughout the centuries.
Learning and interpreting asymmetry-labeled DAGs: a case study on COVID-19 fear
Bayesian networks are widely used to learn and reason about the dependence structure of discrete variables.
Highly Efficient Structural Learning of Sparse Staged Trees
Several structural learning algorithms for staged tree models, an asymmetric extension of Bayesian networks, have been defined.
Structural Learning of Simple Staged Trees
Bayesian networks faithfully represent the symmetric conditional independences existing between the components of a random vector.
Staged trees and asymmetry-labeled DAGs
Bayesian networks are a widely-used class of probabilistic graphical models capable of representing symmetric conditional independence between variables of interest using the topology of the underlying graph.
Context-Specific Causal Discovery for Categorical Data Using Staged Trees
Causal discovery algorithms aim at untangling complex causal relationships from data.
A new class of generative classifiers based on staged tree models
Generative models for classification use the joint probability distribution of the class variable and the features to construct a decision rule.
Learning DAGs without imposing acyclicity
We explore if it is possible to learn a directed acyclic graph (DAG) from data without imposing explicitly the acyclicity constraint.
Sparse Cholesky covariance parametrization for recovering latent structure in ordered data
The sparse Cholesky parametrization of the inverse covariance matrix can be interpreted as a Gaussian Bayesian network; however its counterpart, the covariance Cholesky factor, has received, with few notable exceptions, little attention so far, despite having a natural interpretation as a hidden variable model for ordered signal data.
Graphical continuous Lyapunov models
The linear Lyapunov equation of a covariance matrix parametrizes the equilibrium covariance matrix of a stochastic process.
The R Package stagedtrees for Structural Learning of Stratified Staged Trees
stagedtrees is an R package which includes several algorithms for learning the structure of staged trees and chain event graphs from data.
Causal structure learning from time series: Large regression coefficients may predict causal links better in practice than small p-values
In this article, we describe the algorithms for causal structure learning from time series data that won the Causality 4 Climate competition at the Conference on Neural Information Processing Systems 2019 (NeurIPS).
Markov Property in Generative Classifiers
We show that, for generative classifiers, conditional independence corresponds to linear constraints for the induced discrimination functions.
