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We propose a new general methodology, Explainer Divergence Scores (EDS), to evaluate Post-Hoc Explanations for the purpose of identifying spurious correlations in neural networks. We use our methodology to compare the detection performance of three different explainers - feature attribution methods, influential examples and concept extraction, on two different image datasets.