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Found 2 papers, 1 papers with code
$\texttt{causalAssembly}$: Generating Realistic Production Data for Benchmarking Causal Discovery
We use the assembly line data and associated ground truth information to build a system for generation of semisynthetic manufacturing data that supports benchmarking of causal discovery methods.
High-Dimensional Undirected Graphical Models for Arbitrary Mixed Data
Methods for learning such graphical models are well developed in the case where all variables are either continuous or discrete, including in high-dimensions.
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