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Found 3 papers, 1 papers with code
MCCE: Monte Carlo sampling of realistic counterfactual explanations
We introduce MCCE: Monte Carlo sampling of valid and realistic Counterfactual Explanations for tabular data, a novel counterfactual explanation method that generates on-manifold, actionable and valid counterfactuals by modeling the joint distribution of the mutable features given the immutable features and the decision.
groupShapley: Efficient prediction explanation with Shapley values for feature groups
The main drawback with Shapley values, however, is that its computational complexity grows exponentially in the number of input features, making it unfeasible in many real world situations where there could be hundreds or thousands of features.
Explaining predictive models with mixed features using Shapley values and conditional inference trees
It is becoming increasingly important to explain complex, black-box machine learning models.
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