Noa Ben David

Structural Causal Model Variational Autoencoders for Distribution-Shift-Robust Representation Learning: Identifiability Theory, Intervention Generalization, and CausalBench Evaluation

This study investigates structural causal model VAE (SCM-VAE) framework for learning identifiable causal representations that generalize under distribution shift, with theoretical identifiability guarantees and CausalBench out-of-distribution evaluation within the context of machine learning theory and causal inference...