Missingness-aware Causal Concept Explainer: An Elegant Explanation by Researchers to Solve Causal Effect Limitations in Black Box Interpretability

Concept-based explanations of machine learning applications have a greater intuitive appeal, as established by emerging research as an alternative to traditional approaches. Concept-driven methods explain the decisions of a model by aligning its representation with human understandable concepts. Conventional approaches to ML explainability attribute a model’s behavior to low-level features of the input, whereas concept-based […]

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