Ordering-BasedCausalDiscoveryforLinearand NonlinearRelations

Most current methods restrict their analysis to datasets that are assumed to have pure linear or nonlinear relations, which isoften not reflectiveofreal-world datasets that contain acombination of both. This paper presents CaPS, an ordering-based causal discovery algorithm that effectively handles linear and nonlinear relations.