1 citations · 2 across the 6 of their papers we have counts for
6 papers
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…
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…
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…
Robust learning of staged tree models: A case study in evaluating transport services
Manuele Leonelli, Gherardo Varando
Staged trees are a relatively recent class of probabilistic graphical models that extend Bayesian networks to formally and graphically account for non-symmetric patterns of depende…
Using Staged Tree Models for Health Data: Investigating Invasive Fungal Infections by Aspergillus and Other Filamentous Fungi
Maria Teresa Filigheddu, Manuele Leonelli, Gherardo Varando +4
Machine learning models are increasingly used in the medical domain to study the association between risk factors and diseases to support practitioners in predicting health outcome…
Discovering Causal Relations and Equations from Data
Gustau Camps-Valls, Andreas Gerhardus, Urmi Ninad +7
Physics is a field of science that has traditionally used the scientific method to answer questions about why natural phenomena occur and to make testable models that explain the p…