Higher-Order Causal Structure Learning with Additive Models

TL;DR

This paper extends causal additive models to include higher-order interactions represented by hyper DAGs, providing theoretical foundations and a practical algorithm that improves causal discovery in complex systems.

Contribution

It introduces a novel higher-order causal structure model using hyper DAGs, with theoretical identifiability results and an extended greedy algorithm for learning these structures.

Findings

Hyper DAGs extend causal modeling to higher-order interactions.

Theoretical identifiability of hyper DAGs is established.

Empirical results show improved causal discovery with the new method.

Abstract

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.