Score-based Greedy Search for Structure Identification of Partially Observed Linear Causal Models
This addresses the challenge of causal discovery in partially observed systems for scientific fields, offering a novel method to mitigate issues like multiple testing and error propagation.
The authors tackled the problem of identifying causal structures with latent variables by proposing the first score-based greedy search method, which achieves global consistency and efficiently finds the optimal structure, as validated on synthetic and real-life data.
Identifying the structure of a partially observed causal system is essential to various scientific fields. Recent advances have focused on constraint-based causal discovery to solve this problem, and yet in practice these methods often face challenges related to multiple testing and error propagation. These issues could be mitigated by a score-based method and thus it has raised great attention whether there exists a score-based greedy search method that can handle the partially observed scenario. In this work, we propose the first score-based greedy search method for the identification of structure involving latent variables with identifiability guarantees. Specifically, we propose Generalized N Factor Model and establish the global consistency: the true structure including latent variables can be identified up to the Markov equivalence class by using score. We then design Latent variable Greedy Equivalence Search (LGES), a greedy search algorithm for this class of model with well-defined operators, which search very efficiently over the graph space to find the optimal structure. Our experiments on both synthetic and real-life data validate the effectiveness of our method (code will be publicly available).