Abstract
We present GLOBAL CAUSAL ANALYSIS (GCA) for text classification. GCA is a technique for global model-agnostic explainability drawing from well-established observational causal structure learning algorithms. GCA generates an explanatory graph from high-level human-interpretable features, revealing how these features affect each other and the black-box output. We show how these high-level
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