Coresets for fast causal discovery with the additive noise model

Boxiang Zhao, Shuliang Wang, Lianhua Chi, Hanning Yuan, Ye Yuan, Qi Li, Jing Geng, Shao-Liang Zhang

Abstract: Highlights•New coresets proposed for the additive noise model greatly reduces the data size for causal discovery.•A time-efficient algorithm, FANM, is proposed for causal discovery based on the coresets.•The coreset construction is applied to causal graph learning algorithm, CDCSG, to show its effectiveness.•The proposed FANM is verified by both synthetic data and real-world data.