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…