While large-scale pretraining brings remarkable capabilities, it cannot fundamentally rewrite the architecture’s core inductive biases.

We provide a faster algorithm for generic identification in tree-shaped linear structural causal models.

We propose a causal generative model that accurately estimates a broad class of causal queries, including counterfactuals, in the presence of hidden confounders.