4 papers
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…
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…
Proliferating active disks with game dynamical interaction
Alejandro Almodóvar, Tobias Galla, Cristóbal López
We study a system of self-propelled, proliferating finite-size disks with game-theoretical interactions, where growth rates depend on local population composition. We analyze how t…
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…