Higher-Order Causal Structure Learning with Additive Models
This work addresses a gap in causal discovery for real-world processes with higher-order mechanisms, though it is incremental as it builds on existing causal additive models.
The paper tackles the problem of causal structure learning for processes with higher-order interactions by extending causal additive models to include such interactions, represented as directed acyclic hypergraphs, and demonstrates empirical usefulness in synthetic experiments.
Causal structure learning has long been the central task of inferring causal insights from data. Despite the abundance of real-world processes exhibiting higher-order mechanisms, however, an explicit treatment of interactions in causal discovery has received little attention. In this work, we focus on extending the causal additive model (CAM) to additive models with higher-order interactions. This second level of modularity we introduce to the structure learning problem is most easily represented by a directed acyclic hypergraph which extends the DAG. We introduce the necessary definitions and theoretical tools to handle the novel structure we introduce and then provide identifiability results for the hyper DAG, extending the typical Markov equivalence classes. We next provide insights into why learning the more complex hypergraph structure may actually lead to better empirical results. In particular, more restrictive assumptions like CAM correspond to easier-to-learn hyper DAGs and better finite sample complexity. We finally develop an extension of the greedy CAM algorithm which can handle the more complex hyper DAG search space and demonstrate its empirical usefulness in synthetic experiments.