ALICE: Amortized Learning for Invariance-aware Causal Estimation

Causal Discovery (CD) is fundamental across domains such as biology, finance, weather, and physical systems. Classical CD approaches often rely on restrictive assumptions about causal structure, noise distributions, and mechanisms, and require sufficiently large datasets for reliable estimation. These factors can limit their effectiveness on real-world datasets. Moreover, classical and existing zero-shot approaches do not explicitly exploit feature semantics, which can provide complementary causal information. To this end, we first introduce Amortized Learning for Invariance-aware Causal Estimation (ALICE), a causal discovery foundation model, trained entirely on synthetic datasets generated from diverse structural causal models, that maps observational data to a full directed causal graph in a single forward pass. Our diverse pretraining corpus spanning graph families, mechanisms, and noise processes enables effective zero-shot transfer to real-world datasets. Second, we propose targeted augmentations and consistency losses to enforce key graph invariances, including realization, permutation, and scale. Leveraging these invariances, we propose test-time optimization strategies that improve the F1 score by up to 14%. Finally, we incorporate feature semantics as a post-processing signal, yielding up to 18% improvement on real-world causal estimation tasks. Across synthetic, biological, physical-system, bivariate, and tabular benchmarks, ALICE achieves accurate and robust causal graph estimation without dataset-specific training.