Bayesian ICA for Causal Discovery

Abstract

LiNGAM identifies causal orders by exploiting the non-Gaussianity and mutual independence of structural disturbances. Several extensions allow particular forms of latent confounding, but a general criterion for comparing complete causal orders when the disturbances are dependent remains lacking. We formulate ICA in a Bayesian manner and propose Bayesian LiNGAM. For each candidate order, we use total correlation, or multivariate mutual information, among the order-dependent disturbances as a quantitative measure of confounding and estimate it from data by Bayesian marginal likelihoods. The causal order is selected by globally minimizing this estimate. We prove the consistency of the estimator and implement the global optimization using shortest-path search and dynamic programming. In the no-confounding large-sample regime, the search asymptotically requires the same number of score evaluations as Direct-LiNGAM. Bayesian LiNGAM therefore directly and globally optimizes the original ICA dependence criterion, rather than relying on greedy local decisions or a negentropy-based surrogate. Numerical experiments show that it generally achieves better causal-order recovery, particularly when latent confounding is present.