rafael cabañas

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I am assitant professor in Statistics at the Department of Mathematics at the University of Almería (Spain). I have broad interests in probabilistic graphical models, machine learning, causality and counterfactual reasoning.

rcabanas@ual.es

Publications

I highlight my most relevant publications. You can visit my google scholar profile for the complete list.

  1. Bjøru, A. R., Cabañas, R., Langseth, H., & Salmerón, A. (2025). Divide and Conquer for Causal Computation. International Journal of Approximate Reasoning, 109520.
    COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE - SCIE [Q2]

  2. Cabañas, R., Maldonado, A. D., Morales, M., Aguilera, P. A., & Salmerón, A. (2025). Bayesian networks for causal analysis in socioecological systems. Ecological Informatics, 103173.
    ECOLOGY - SCIE [Q1]

  3. Balordi, A., Bernasconi, A., Andreotti, A., Guzzinati, S., Cabañas De Paz, R., & Zanga, A. (2025). On Counterfactual Explanations of Cardiovascular Risk in Adolescent and Young Adult Breast Cancer Survivors. Journal of Medical Systems, 49(1), 140.
    MEDICAL INFORMATICS - SCIE [Q1]

  4. Zaffalon, M., Antonucci, A., Cabañas, R., Huber, D., & Azzimonti, D. (2024). Efficient computation of counterfactual bounds. International Journal of Approximate Reasoning, 109111
    COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE - SCIE [Q2]

  5. Masegosa, A. R., Cabañas, R., Maldonado, A. D., & Morales, M. (2024). Learning Styles Impact Students’ Perceptions on Active Learning Methodologies: A Case Study on the Use of Live Coding and Short Programming Exercises. Education Sciences, 14(3), 250.
    EDUCATION & EDUCATIONAL RESEARCH - SCIE [Q1]

  6. Zaffalon, M., Antonucci, A., Cabañas, R., & Huber, D. (2023). Approximating counterfactual bounds while fusing observational, biased and randomised data sources. International Journal of Approximate Reasoning, 162, 109023.
    COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE - SCIE [Q2]

  7. L.A. Ortega, Cabañas, R., A.R. Masegosa. (2022). Diversity and Generalization in Neural Network Ensembles. In International Conference on Artificial Intelligence and Statistics PMLR
    Conference paper [Core A]

  8. Masegosa, A. R., Cabañas, R., Lanseth, H., Nielsen, T. D., and Salmerón, A. (2021). Probabilistic Models with Deep Neural Networks. Entropy, 23, 117
    PHYSICS, MULTIDISCIPLINARY - SCIE [Q2]

  9. Gómez-Olmedo, M., Cabañas, R., Cano, A., Moral, S., and Retamero, O. P. (2021). Value-Based Potentials: Exploiting Quantitative Information Regularity Patterns in Probabilistic Graphical Models. International Journal of Intelligent Systems.
    COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE - SCIE [Q1]

  10. Cózar, J., Cabañas, R., Salmerón, A., and Masegosa, A. R. (2020). InferPy: Probabilistic modeling with deep neural networks made easy. Neurocomputing, 415, 408-410.
    COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE - SCIE [Q1]

  11. Cabañas, R., Salmerón, A., & Masegosa, A. R. (2019). InferPy: Probabilistic modeling with Tensorflow made easy. Knowledge-Based Systems, 168, 25-27.
    COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE - SCIE [Q1]

  12. Masegosa, A. R., Martinez, A. M., Ramos-López, D., Cabañas, R., Salmerón, A., Langseth, H., ... & Madsen, A. L. (2019). AMIDST: A Java toolbox for scalable probabilistic machine learning. Knowledge-Based Systems, 163, 595-597.
    COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE - SCIE [Q1]

  13. Cabañas, R., Antonucci, A., Cano, A., & Gómez-Olmedo, M. (2017). Evaluating interval-valued influence diagrams. International Journal of Approximate Reasoning, 80, 393-411.
    COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE - SCIE [Q2]

  14. Cabañas, R., Gómez-Olmedo, M., & Cano, A. (2016). Using binary trees for the evaluation of influence diagrams. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 24(01), 59-89.
    COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE - SCIE [Q3]

  15. Cabañas, R., Cano, A., Gómez-Olmedo, M., & Madsen, A. L. (2016). Improvements to variable elimination and symbolic probabilistic inference for evaluating influence diagrams. International Journal of Approximate Reasoning, 70, 13-35.
    COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE - SCIE [Q2]