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AcceleratedLiNGAM: Learning Causal DAGs on GPUs
By efficiently parallelizing existing causal discovery methods, AcceleratedLiNGAM scales to thousands of dimensions, making it practical for large-scale problems. The main thesis is to accelerate LiNGAM analysis using GPU parallelization for efficient causal inference.
Recursive Causal Discovery: Unveiling the Concept of Removable Variables in Causal Graphs
Removable variables enable efficient causal discovery by reducing problem size and improving statistical reliability.
AcceleratedLiNGAM: Learning Causal DAGs at the Speed of GPUs
Existing causal discovery methods are slow, but AcceleratedLiNGAM scales them efficiently using GPUs.
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