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Prior work has shown that causal structure can be uniquely identified fromobservational data when these follow a structural equation model whose errorterms have equal variances. We show that this fact is implied by an orderingamong (conditional) variances. We demonstrate that ordering estimates of thesevariances yields a simple yet state-of-the-art method for causal structurelearning that is readily extendable to high-dimensional problems.

On Causal Discovery with Equal Variance Assumption