arXiv stat.ML
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A Step Towards Inherently Interpretable Causal Machine Learning Models For Decision Support
arXiv:2606.24348v1 Announce Type: cross Abstract: The growing reliance on machine learning for decisions across sectors underscores the importance of model transparency and interpretability. Existing post hoc explainability methods and inherently interpretable approaches shed light on model behavior, yet they primarily