3 papers
stat.ML2026
Causal Representation Learning for Generalisable Recommendation
Yorgos Felekis, Michael O'Riordan, Oriol Corcoll +1
Predictive models trained on observational data often fail to generalise to the distributions they encounter when deployed, especially when the training data is a product of the sy…
cs.LG2026
Distributionally Robust Causal Abstractions
Yorgos Felekis, Theodoros Damoulas, Paris Giampouras
Causal Abstraction (CA) theory provides a principled framework for relating causal models that describe the same system at different levels of granularity while ensuring interventi…
cs.LG2025
Causal Abstraction Learning based on the Semantic Embedding Principle
Gabriele D'Acunto, Fabio Massimo Zennaro, Yorgos Felekis +1
Structural causal models (SCMs) allow us to investigate complex systems at multiple levels of resolution. The causal abstraction (CA) framework formalizes the mapping between high-…