Learning Cyclic Causal Models from Incomplete Data

arXiv:2402.15625v1 Announce Type: cross
Abstract: Causal learning is a fundamental problem in statistics and science, offering insights into predicting the effects of unseen treatments on a system. Despite recent advances in this topic, most existing causal discovery algorithms operate under two key assumptions: (i) the underlying graph is acyclic, and (ii) the available data is complete. These assumptions can be problematic as many real-world systems contain feedback loops (e.g., biological systems), and practical scenarios frequently involve missing data. In this …

abstract advances algorithms arxiv assumptions cs.ai cs.lg data discovery effects graph incomplete data insights key science statistics stat.ml type