Coordinated Multi-Neighborhood Learning on a Directed Acyclic Graph

TL;DR

This paper introduces a new method for local causal structure learning around specific target nodes in a DAG, improving accuracy and efficiency without reconstructing the entire graph.

Contribution

It develops a constraint-based algorithm for local neighborhood estimation around multiple target nodes, with proven consistency and practical advantages.

Findings

More accurate neighborhood learning on synthetic data

Reduced computational cost compared to full DAG estimation

Effective on real-world datasets

Abstract

Learning the structure of causal directed acyclic graphs (DAGs) is useful in many areas of machine learning and artificial intelligence, with wide applications. However, in the high-dimensional setting, it is challenging to obtain good empirical and theoretical results without strong and often restrictive assumptions. Additionally, it is questionable whether all of the variables purported to be included in the network are observable. It is of interest then to restrict consideration to a subset of the variables for relevant and reliable inferences. In fact, researchers in various disciplines can usually select a set of target nodes in the network for causal discovery. This paper develops a new constraint-based method for estimating the local structure around multiple user-specified target nodes, enabling coordination in structure learning between neighborhoods. Our method facilitates causal discovery without learning the entire DAG structure. We establish consistency results for our algorithm with respect to the local neighborhood structure of the target nodes in the true graph. Experimental results on synthetic and real-world data show that our algorithm is more accurate in learning the neighborhood structures with much less computational cost than standard methods that estimate the entire DAG. An R package implementing our methods may be accessed at https://github.com/stephenvsmith/CML.