$\texttt{causalAssembly}$: Generating Realistic Production Data for Benchmarking Causal Discovery

arXiv:2306.10816v2 Announce Type: replace-cross
Abstract: Algorithms for causal discovery have recently undergone rapid advances and increasingly draw on flexible nonparametric methods to process complex data. With these advances comes a need for adequate empirical validation of the causal relationships learned by different algorithms. However, for most real data sources true causal relations remain unknown. This issue is further compounded by privacy concerns surrounding the release of suitable high-quality data. To help address these challenges, we gather a complex dataset comprising …

abstract advances algorithms arxiv benchmarking cs.lg data data sources discovery process production production data real data relationships stat.me stat.ml true type validation