A survey of causal discovery based on functional causal model

Causal discovery finds widespread applications, ranging from estimating treatment effectiveness in medicine, analyzing policy impacts in economics, to constructing predictive models in machine learning—all of which rely on the study and discovery of causal relationships. In recent years, as causal learning has progressed, causal discovery has been classified into different categories depending on assumptions and learning strategies. In this paper, we undertake an exploration of causal discovery methods based on the functional causal model (FCM). We commence by introducing essential terminology associated with causal discovery and laying out the foundational assumptions underpinning FCM-based methods. Following this, we conduct a comprehensive exploration of classical FCM algorithms that have gained prominence in recent years. Furthermore, we scrutinize the performance of these FCM methods across a selection of benchmark datasets. Finally, we deliberate on unresolved issues within this category of methodologies and outline potential avenues for future research.