Proves that causal representation learning is possible with far fewer environments and unknown intervention targets than previously assumed.
March 30, 2026
Original Paper
Beyond identifiability: Learning causal representations with few environments and finite samples
arXiv · 2603.25796
The Takeaway
It provides a theoretical foundation for recovering interpretable causal models from high-dimensional data using only a logarithmic number of interventions. This moves causal discovery from a data-hungry theoretical exercise toward a more computationally feasible reality for identifying the latent drivers of complex systems.
From the abstract
We provide explicit, finite-sample guarantees for learning causal representations from data with a sublinear number of environments. Causal representation learning seeks to provide a rigourous foundation for the general representation learning problem by bridging causal models with latent factor models in order to learn interpretable representations with causal semantics. Despite a blossoming theory of identifiability in causal representation learning, estimation and finite-sample bounds are les