Approximate Lazy Evaluation of Influence Diagrams. - Rafael Cabañas 001; Andrés Cano; Manuel Gómez-Olmedo; Anders L. Madsen (CAEPIA, 2013)

Evaluating Asymmetric Decision Problems with Binary Constraint Trees. - Rafael Cabañas 001; Manuel Gómez-Olmedo; Andrés Cano (ECSQARU, 2013)

Heuristics for Determining the Elimination Ordering in the Influence Diagram Evaluation with Binary Trees. - Rafael Cabañas 001; Andrés Cano; Manuel Gómez-Olmedo; Anders L. Madsen (SCAI, 2013)

On SPI for Evaluating Influence Diagrams. - Rafael Cabañas 001; Anders L. Madsen; Andrés Cano; Manuel Gómez-Olmedo (IPMU (1), 2014)

On SPI-Lazy Evaluation of Influence Diagrams. - Rafael Cabañas 001; Andrés Cano; Manuel Gómez-Olmedo; Anders L. Madsen (Probabilistic Graphical Models, 2014)

Variable Elimination for Interval-Valued Influence Diagrams. - Rafael Cabañas 001; Alessandro Antonucci 001; Andrés Cano; Manuel Gómez-Olmedo (ECSQARU, 2015)

Financial Data Analysis with PGMs Using AMIDST. - Rafael Cabañas 001; Ana M. Martínez; Andrés R. Masegosa; Darío Ramos-López; Antonio Salmerón; Thomas D. Nielsen; Helge Langseth; Anders L. Madsen (ICDM Workshops, 2016)

Improvements to Variable Elimination and Symbolic Probabilistic Inference for evaluating Influence Diagrams. - Rafael Cabañas 001; Andrés Cano; Manuel Gómez-Olmedo; Anders L. Madsen (Int. J. Approx. Reason., 2016)

Using Binary Trees for the Evaluation of Influence Diagrams. - Rafael Cabañas 001; Manuel Gómez-Olmedo; Andrés Cano (Int. J. Uncertain. Fuzziness Knowl. Based Syst., 2016)

Evaluating interval-valued influence diagrams. - Rafael Cabañas 001; Alessandro Antonucci 001; Andrés Cano; Manuel Gómez-Olmedo (Int. J. Approx. Reason., 2017)

Virtual Subconcept Drift Detection in Discrete Data Using Probabilistic Graphical Models. - Rafael Cabañas 001; Andrés Cano; Manuel Gómez-Olmedo; Andrés R. Masegosa; Serafín Moral (IPMU (3), 2018)

InferPy: Probabilistic modeling with Tensorflow made easy. - Rafael Cabañas 001; Antonio Salmerón; Andrés R. Masegosa (Knowl. Based Syst., 2019)

AMIDST: A Java toolbox for scalable probabilistic machine learning. - Andrés R. Masegosa; Ana M. Martínez; Darío Ramos-López; Rafael Cabañas 001; Antonio Salmerón; Helge Langseth; Thomas D. Nielsen; Anders L. Madsen (Knowl. Based Syst., 2019)

CREDICI: A Java Library for Causal Inference by Credal Networks. - Rafael Cabañas 001; Alessandro Antonucci 001; David Huber 001; Marco Zaffalon (PGM, 2020)

Probabilistic Graphical Models with Neural Networks in InferPy. - Rafael Cabañas 001; Javier Cózar; Antonio Salmerón; Andrés R. Masegosa (PGM, 2020)

CREMA: A Java Library for Credal Network Inference. - David Huber 001; Rafael Cabañas 001; Alessandro Antonucci 001; Marco Zaffalon (PGM, 2020)

Structural Causal Models Are (Solvable by) Credal Networks. - Marco Zaffalon; Alessandro Antonucci 001; Rafael Cabañas 001 (PGM, 2020)

EM Based Bounding of Unidentifiable Queries in Structural Causal Models. - Marco Zaffalon; Alessandro Antonucci 001; Rafael Cabañas 001 (CoRR, 2020)

InferPy: Probabilistic modeling with deep neural networks made easy. - Javier Cózar; Rafael Cabañas 001; Antonio Salmerón; Andrés R. Masegosa (Neurocomputing, 2020)

CREPO: An Open Repository to Benchmark Credal Network Algorithms. - Rafael Cabañas 001; Alessandro Antonucci 001 (ISIPTA, 2021)

Probabilistic Models with Deep Neural Networks. - Andrés R. Masegosa; Rafael Cabañas 001; Helge Langseth; Thomas D. Nielsen; Antonio Salmerón (Entropy, 2021)

Value-based potentials: Exploiting quantitative information regularity patterns in probabilistic graphical models. - Manuel Gómez-Olmedo; Rafael Cabañas 001; Andrés Cano; Serafín Moral; Ofelia P. Retamero (Int. J. Intell. Syst., 2021)

Diversity and Generalization in Neural Network Ensembles. - Luis A. Ortega 001; Rafael Cabañas 001; Andrés R. Masegosa (AISTATS, 2022)

Bounding Counterfactuals under Selection Bias. - Marco Zaffalon; Alessandro Antonucci 001; Rafael Cabañas 001; David Huber 001; Dario Azzimonti (PGM, 2022)

Learning to Bound Counterfactual Inference in Structural Causal Models from Observational and Randomised Data. - Marco Zaffalon; Alessandro Antonucci 001; David Huber 001; Rafael Cabañas 001 (CoRR, 2022)

Approximating counterfactual bounds while fusing observational, biased and randomised data sources. - Marco Zaffalon; Alessandro Antonucci 001; Rafael Cabañas 001; David Huber 001 (Int. J. Approx. Reason., 2023)

On Counterfactual Explainations of Cardiovascular Risk in Adolescents and Young Adults Breast Cancer Survivors. - Alice Bernasconi; Antonio Balordi; Alessio Zanga; Rafael Cabañas 001 (HC@AIxIA, 2024)

A Divide and Conquer Approach for Solving Structural Causal Models. - Anna Rodum Bjøru; Rafael Cabañas 001; Helge Langseth; Antonio Salmerón (PGM, 2024)

Counterfactual Reasoning with Probabilistic Graphical Models for Analyzing Socioecological Systems. - Rafael Cabañas 001; Ana D. Maldonado; María Morales; Pedro Aguilera Aguilera; Antonio Salmerón (CoRR, 2024)

How do Machine Learning Models Change? - Joel Castaño; Rafael Cabañas 001; Antonio Salmerón; David Lo 001; Silverio Martínez-Fernández (CoRR, 2024)

Efficient computation of counterfactual bounds. - Marco Zaffalon; Alessandro Antonucci 001; Rafael Cabañas 001; David Huber 001; Dario Azzimonti (Int. J. Approx. Reason., 2024)

Replication Package for 'How do Machine Learning Models Change?'. - Joel Castaño Fernández; Rafael Cabañas 001; Antonio Salmerón; David Lo 001; Silverio Martínez-Fernández (Zenodo, 2024)

Replication Package for 'How do Machine Learning Models Change?'. - Joel Castaño Fernández; Rafael Cabañas 001; Antonio Salmerón; David Lo 001; Silverio Martínez-Fernández (Zenodo, 2024)

Bayesian networks for causal analysis in socioecological systems. - Rafael Cabañas 001; Ana D. Maldonado; María Morales; Pedro Aguilera Aguilera; Antonio Salmerón (Ecol. Informatics, 2025)

Divide and conquer for causal computation. - Anna Rodum Bjøru; Rafael Cabañas 001; Helge Langseth; Antonio Salmerón (Int. J. Approx. Reason., 2025)