7 citations · 50 across the 26 of their papers we have counts for
26 papers
Staged Event Trees for Transparent Treatment Effect Estimation
Gherardo Varando, Manuele Leonelli, Jordi Cerdà-Bautista +2
Average and conditional treatment effects are fundamental causal quantities used to evaluate the effectiveness of treatments in various critical applications, including clinical se…
Learning Causal Response Representations through Direct Effect Analysis
Homer Durand, Gherardo Varando, Gustau Camps-Valls
We propose a novel approach for learning causal response representations. Our method aims to extract directions in which a multidimensional outcome is most directly caused by a tre…
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…
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…
Learning and interpreting asymmetry-labeled DAGs: a case study on COVID-19 fear
Manuele Leonelli, Gherardo Varando
Bayesian networks are widely used to learn and reason about the dependence structure of discrete variables. However, they are only capable of formally encoding symmetric conditiona…
Highly Efficient Structural Learning of Sparse Staged Trees
Manuele Leonelli, Gherardo Varando
Several structural learning algorithms for staged tree models, an asymmetric extension of Bayesian networks, have been defined. However, they do not scale efficiently as the number…
Structural Learning of Simple Staged Trees
Manuele Leonelli, Gherardo Varando
Bayesian networks faithfully represent the symmetric conditional independences existing between the components of a random vector. Staged trees are an extension of Bayesian network…
Staged trees and asymmetry-labeled DAGs
Gherardo Varando, Federico Carli, Manuele Leonelli
Bayesian networks are a widely-used class of probabilistic graphical models capable of representing symmetric conditional independence between variables of interest using the topol…
Context-Specific Causal Discovery for Categorical Data Using Staged Trees
Manuele Leonelli, Gherardo Varando
Causal discovery algorithms aim at untangling complex causal relationships from data. Here, we study causal discovery and inference methods based on staged tree models, which can r…
A new class of generative classifiers based on staged tree models
Federico Carli, Manuele Leonelli, Gherardo Varando
Generative models for classification use the joint probability distribution of the class variable and the features to construct a decision rule. Among generative models, Bayesian n…
Learning DAGs without imposing acyclicity
Gherardo Varando
We explore if it is possible to learn a directed acyclic graph (DAG) from data without imposing explicitly the acyclicity constraint. In particular, for Gaussian distributions, we…
Sparse Cholesky covariance parametrization for recovering latent structure in ordered data
Irene Córdoba, Concha Bielza, Pedro Larrañaga +1
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…
Graphical continuous Lyapunov models
Gherardo Varando, Niels Richard Hansen
The linear Lyapunov equation of a covariance matrix parametrizes the equilibrium covariance matrix of a stochastic process. This parametrization can be interpreted as a new graphic…
The R Package stagedtrees for Structural Learning of Stratified Staged Trees
Federico Carli, Manuele Leonelli, Eva Riccomagno +1
stagedtrees is an R package which includes several algorithms for learning the structure of staged trees and chain event graphs from data. Score-based and clustering-based algorith…
Causal structure learning from time series: Large regression coefficients may predict causal links better in practice than small p-values
Sebastian Weichwald, Martin E Jakobsen, Phillip B Mogensen +3
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…
On generating random Gaussian graphical models
Irene Córdoba, Gherardo Varando, Concha Bielza +1
Structure learning methods for covariance and concentration graphs are often validated on synthetic models, usually obtained by randomly generating: (i) an undirected graph, and (i…
Markov Property in Generative Classifiers
Gherardo Varando, Concha Bielza, Pedro Larrañaga +1
We show that, for generative classifiers, conditional independence corresponds to linear constraints for the induced discrimination functions. Discrimination functions of undirecte…