17 citations · 26 across the 10 of their papers we have counts for
12 papers
Causal Modelling of Support Interventions for Student Competency Assessment
Francesca Mangili, Alessandro Antonucci, Rafael Cabañas
Accurate assessment of student competencies is essential for enabling educators to identify individual needs, design targeted interventions, and evaluate the effectiveness of educa…
Approximating Counterfactual Bounds while Fusing Observational, Biased and Randomised Data Sources
Marco Zaffalon, Alessandro Antonucci, Rafael Cabañas +1
We address the problem of integrating data from multiple, possibly biased, observational and interventional studies, to eventually compute counterfactuals in structural causal mode…
Efficient Computation of Counterfactual Bounds
Marco Zaffalon, Alessandro Antonucci, Rafael Cabañas +2
We assume to be given structural equations over discrete variables inducing a directed acyclic graph, namely, a structural causal model, together with data about its internal nodes…
Learning to Bound Counterfactual Inference from Observational, Biased and Randomised Data
Marco Zaffalon, Alessandro Antonucci, David Huber +1
We address the problem of integrating data from multiple, possibly biased, observational and interventional studies, to eventually compute counterfactuals in structural causal mode…
Bounding Counterfactuals under Selection Bias
Marco Zaffalon, Alessandro Antonucci, Rafael Cabañas +2
Causal analysis may be affected by selection bias, which is defined as the systematic exclusion of data from a certain subpopulation. Previous work in this area focused on the deri…
Diversity and Generalization in Neural Network Ensembles
Luis A. Ortega, Rafael Cabañas, Andrés R. Masegosa
Ensembles are widely used in machine learning and, usually, provide state-of-the-art performance in many prediction tasks. From the very beginning, the diversity of an ensemble has…
CREPO: An Open Repository to Benchmark Credal Network Algorithms
Rafael Cabañas, Alessandro Antonucci
Credal networks are a popular class of imprecise probabilistic graphical models obtained as a Bayesian network generalization based on, so-called credal, sets of probability mass f…
Causal Expectation-Maximisation
Marco Zaffalon, Alessandro Antonucci, Rafael Cabañas
Structural causal models are the basic modelling unit in Pearl's causal theory; in principle they allow us to solve counterfactuals, which are at the top rung of the ladder of caus…
Structural Causal Models Are (Solvable by) Credal Networks
Marco Zaffalon, Alessandro Antonucci, Rafael Cabañas
A structural causal model is made of endogenous (manifest) and exogenous (latent) variables. We show that endogenous observations induce linear constraints on the probabilities of…
Probabilistic Models with Deep Neural Networks
Andrés R. Masegosa, Rafael Cabañas, Helge Langseth +2
Recent advances in statistical inference have significantly expanded the toolbox of probabilistic modeling. Historically, probabilistic modeling has been constrained to (i) very re…
InferPy: Probabilistic Modeling with Deep Neural Networks Made Easy
Javier Cózar, Rafael Cabañas, Antonio Salmerón +1
InferPy is a Python package for probabilistic modeling with deep neural networks. It defines a user-friendly API that trades-off model complexity with ease of use, unlike other lib…
AMIDST: a Java Toolbox for Scalable Probabilistic Machine Learning
Andrés R. Masegosa, Ana M. Martínez, Darío Ramos-López +5
The AMIDST Toolbox is a software for scalable probabilistic machine learning with a spe- cial focus on (massive) streaming data. The toolbox supports a flexible modeling language b…