Weakly-supervised causal discovery based on fuzzy knowledge and complex data complementarity

arXiv:2405.08699v1 Announce Type: cross
Abstract: Causal discovery based on observational data is important for deciphering the causal mechanism behind complex systems. However, the effectiveness of existing causal discovery methods is limited due to inferior prior knowledge, domain inconsistencies, and the challenges of high-dimensional datasets with small sample sizes. To address this gap, we propose a novel weakly-supervised fuzzy knowledge and data co-driven causal discovery method named KEEL. KEEL adopts a fuzzy causal knowledge schema to encapsulate diverse types of fuzzy …

abstract arxiv causal challenges complex systems cs.lg data datasets discovery domain however knowledge prior sample small stat.ml systems type weakly-supervised