3 papers
stat.ML2025
CausalKANs: interpretable treatment effect estimation with Kolmogorov-Arnold networks
Alejandro Almodóvar, Patricia A. Apellániz, Santiago Zazo +1
Deep neural networks achieve state-of-the-art performance in estimating heterogeneous treatment effects, but their opacity limits trust and adoption in sensitive domains such as me…
cs.LG2025
Deep Survival Analysis in Multimodal Medical Data: A Parametric and Probabilistic Approach with Competing Risks
Alba Garrido, Alejandro Almodóvar, Patricia A. Apellániz +2
Accurate survival prediction is critical in oncology for prognosis and treatment planning. Traditional approaches often rely on a single data modality, limiting their ability to ca…
cs.LG2025
DeCaFlow: A deconfounding causal generative model
Alejandro Almodóvar, Adrián Javaloy, Juan Parras +2
We introduce DeCaFlow, a deconfounding causal generative model. Training once per dataset using just observational data and the underlying causal graph, DeCaFlow enables accurate c…