Beyond identifiability: Learning causal representations with few environments and finite samples

Beyond identifiability: Learning causal representations with few environments and finite samples explores Develops theoretical guarantees for learning causal representations from limited data and interventions, improving interpretability and robustness.. Commercial viability score: 3/10 in Causal Representation Learning.