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Found 4 papers, 2 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.
Statistical embedding: Beyond principal components
Another type of data sets with a tremendous growth is very high-dimensional network data.
Explaining predictive models using Shapley values and non-parametric vine copulas
In this paper we propose two new approaches for modelling the dependence between the features.
Explaining individual predictions when features are dependent: More accurate approximations to Shapley values
In this paper, we extend the Kernel SHAP method to handle dependent features.
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