5 citations · 18 across the 12 of their papers we have counts for
7 papers · 1 filter
Large Language Models for Constrained-Based Causal Discovery
Kai-Hendrik Cohrs, Gherardo Varando, Emiliano Diaz +2
Causality is essential for understanding complex systems, such as the economy, the brain, and the climate. Constructing causal graphs often relies on either data-driven or expert-d…
Learning Staged Trees from Incomplete Data
Jack Storror Carter, Manuele Leonelli, Eva Riccomagno +1
Staged trees are probabilistic graphical models capable of representing any class of non-symmetric independence via a coloring of its vertices. Several structural learning routines…
Context-Specific Refinements of Bayesian Network Classifiers
Manuele Leonelli, Gherardo Varando
Supervised classification is one of the most ubiquitous tasks in machine learning. Generative classifiers based on Bayesian networks are often used because of their interpretabilit…
Recovering Latent Confounders from High-dimensional Proxy Variables
Nathan Mankovich, Homer Durand, Emiliano Diaz +2
Detecting latent confounders from proxy variables is an essential problem in causal effect estimation. Previous approaches are limited to low-dimensional proxies, sorted proxies, a…
Out-of-distribution robustness for multivariate analysis via causal regularisation
Homer Durand, Gherardo Varando, Nathan Mankovich +1
We propose a regularisation strategy of classical machine learning algorithms rooted in causality that ensures robustness against distribution shifts. Building upon the anchor regr…
Causal hybrid modeling with double machine learning
Kai-Hendrik Cohrs, Gherardo Varando, Nuno Carvalhais +2
Hybrid modeling integrates machine learning with scientific knowledge to enhance interpretability, generalization, and adherence to natural laws. Nevertheless, equifinality and reg…