Proposes combining causal ML with interpretable models to achieve competitive prediction performance and transparency on causal structures for decision support.
Future research should examine how such approaches can be integrated with IIML models
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A Step Towards Inherently Interpretable Causal Machine Learning Models For Decision Support
Proposes combining causal ML with interpretable models to achieve competitive prediction performance and transparency on causal structures for decision support.