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cs.LG2026
Sequential Causal Discovery with Noisy Language Model Priors
Prakhar Verma, David Arbour, Sunav Choudhary +3
Causal discovery from observational data typically assumes access to complete data and availability of perfect domain experts. In practice, data often arrive in batches, are subjec…
cs.LG2025
Relational Causal Discovery with Latent Confounders
Matteo Negro, Andrea Piras, Ragib Ahsan +2
Estimating causal effects from real-world relational data can be challenging when the underlying causal model and potential confounders are unknown. While several causal discovery…