
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.


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AcceleratedLiNGAM: Learning Causal DAGs on GPUs


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Removable variables enable efficient causal discovery by reducing problem size and improving statistical reliability.


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Recursive Causal Discovery: Unveiling the Concept of Removable Variables in Causal Graphs



Existing causal discovery methods are slow, but AcceleratedLiNGAM scales them efficiently using GPUs.


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AcceleratedLiNGAM: Learning Causal DAGs at the Speed of GPUs
