CausalSteward is a multi-agent divide-conquer-combine framework for causal discovery that integrates prior knowledge with data-driven methods in a human-in-the-loop setup for high-dimensional data.
S´ebastien Lachapelle, Philippe Brouillard, Tristan Deleu, and Simon Lacoste-Julien
3 Pith papers cite this work. Polarity classification is still indexing.
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2026 3verdicts
UNVERDICTED 3representative citing papers
GraphGP delivers a linear-scaling GPU implementation of Vecchia's sparse precision approximation for Gaussian processes on arbitrary point sets using a bit-reversed k-d tree and CUDA kernels.
Develops an NNGP spatio-temporal model with SMC squared inference for haplotype frequency estimation from pooled genetic data, demonstrated on 3- and 6-marker antimalarial resistance datasets in Africa.
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A Bayesian spatio-temporal nearest neighbor Gaussian process model for pooled genetic data
Develops an NNGP spatio-temporal model with SMC squared inference for haplotype frequency estimation from pooled genetic data, demonstrated on 3- and 6-marker antimalarial resistance datasets in Africa.