Hierarchical Causal Discovery From Large-Scale Observed Variables

It is a long-standing question to discover causal relations from observed variables in many empirical sciences. However, current causal discovery methods are inefficient when dealing with large-scale observed variables due to challenges in conditional independence (CI) tests or complex computations of acyclicity, and may even fail altogether. To address the efficiency issue in causal discovery from large-scale observed variables, we propose a Hierarchical Causal Discovery (HCD) framework with a bilevel policy that handles this issue by boosting existing models. Specifically, the high-level policy first finds a causal cut set to partition observed variables into several causal clusters and releases the clusters to the low-level policy. The low-level policy applies any causal discovery method to process these causal clusters in parallel and obtain intra-cluster structures for subsequently inter-cluster structure merging in the high-level policy. To avoid missing inter-cluster edges, we theoretically demonstrate the feasibility of causal cluster cut and inter-cluster structure merging. We also prove the completeness and correctness of HCD for causal discovery. Experiments on both synthetic and real-world datasets demonstrate that HCD consistently and significantly enhances the efficiency and effectiveness of existing advanced methods.