A low-rank approximation of kernel-based generalized score functions reduces the computation and memory cost of score-based causal discovery to linear in sample size with comparable accuracy.
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Fast Causal Discovery by Approximate Kernel-based Generalized Score Functions with Linear Computational Complexity
A low-rank approximation of kernel-based generalized score functions reduces the computation and memory cost of score-based causal discovery to linear in sample size with comparable accuracy.