A Framework for Feasible Counterfactual Exploration incorporating Causality, Sparsity and Density
CoRR(2024)
摘要
The imminent need to interpret the output of a Machine Learning model with
counterfactual (CF) explanations - via small perturbations to the input - has
been notable in the research community. Although the variety of CF examples is
important, the aspect of them being feasible at the same time, does not
necessarily apply in their entirety. This work uses different benchmark
datasets to examine through the preservation of the logical causal relations of
their attributes, whether CF examples can be generated after a small amount of
changes to the original input, be feasible and actually useful to the end-user
in a real-world case. To achieve this, we used a black box model as a
classifier, to distinguish the desired from the input class and a Variational
Autoencoder (VAE) to generate feasible CF examples. As an extension, we also
extracted two-dimensional manifolds (one for each dataset) that located the
majority of the feasible examples, a representation that adequately
distinguished them from infeasible ones. For our experimentation we used three
commonly used datasets and we managed to generate feasible and at the same time
sparse, CF examples that satisfy all possible predefined causal constraints, by
confirming their importance with the attributes in a dataset.
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关键词
explainability,counterfactual explanations,feasibility,sparsity,causal relations
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