arXiv:2604.20516v1 Announce Type: new Abstract: Determining identifiability of causal effects from observational data under latent confounding is a central challenge in causal inference. For linear structural causal models, identifiability of causal effects is decidable through symbolic computation. However, standard approaches based on Gr\"obner bases become computationally infeasible beyond smal
Efficient Symbolic Computations for Identifying Causal Effects
Benjamin Hollering, Pratik Misra, Nils Sturma·arXiv stat.ML··1 min read
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