Causal discovery aims to infer cause-and-effect relationships from observational data, a crucial step beyond statistical correlation. A prominent method for this is the Linear Non-Gaussian Acyclic Model (LiNGAM), which can uniquely identify the causal structure by assuming linear relationships and non-Gaussian noise. LiNGAM-based algorithms typically depend on two key components: a search algorithm to determine the causal ordering of variables, and an independence measure to guide the search. Recent work, LiNGAM-MMI, proposed that replacing the simple greedy search with a global, shortest-path search led to superior performance, particularly when unmeasured common causes (confounders) are present. However, the claim was based on experiments that also modified the independence measure from the original baseline, making it difficult to isolate the source of the improvement.
Ong et al. (Thu,) studied this question.