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ICA-based causal discovery methods such as LiNGAM have been highly successful under the assumption that noise variables become independent after an appropriate causal ordering. However, this assumption is often violated in the presence of confounding. \[ \] In this talk, I present a Bayesian and information-theoretic formulation of ICA for causal order estimation that explicitly allows for confounding. Rather than enforcing independence, we quantify residual dependence among noise variables using multivariate mutual information and evaluate causal orders via Bayesian marginal likelihoods. \[ \] This approach provides a principled ranking of causal orders under confounding and recovers classical LiNGAM-type methods as special cases when confounding is absent. I will focus on the conceptual framework and discuss connections to existing ICA-based methods, as well as open questions.