Learning Model-Agnostic Counterfactual Explanations for Tabular Data

21 Oct 2019Martin PawelczykJohannes HaugKlaus BroelemannGjergji Kasneci

Counterfactual explanations can be obtained by identifying the smallest change made to a feature vector to qualitatively influence a prediction; for example, from 'loan rejected' to 'awarded' or from 'high risk of cardiovascular disease' to 'low risk'. Previous approaches often emphasized that counterfactuals should be easily interpretable to humans, motivating sparse solutions with few changes to the feature vectors... (read more)

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