26 citations · 38 across the 3 of their papers we have counts for
3 papers
Disentangling Interactions and Dependencies in Feature Attribution
Gunnar König, Eric Günther, Ulrike von Luxburg
In explainable machine learning, global feature importance methods try to determine how much each individual feature contributes to predicting the target variable, resulting in one…
A Causal Perspective on Meaningful and Robust Algorithmic Recourse
Gunnar König, Timo Freiesleben, Moritz Grosse-Wentrup
Algorithmic recourse explanations inform stakeholders on how to act to revert unfavorable predictions. However, in general ML models do not predict well in interventional distribut…
Relative Feature Importance
Gunnar König, Christoph Molnar, Bernd Bischl +1
Interpretable Machine Learning (IML) methods are used to gain insight into the relevance of a feature of interest for the performance of a model. Commonly used IML methods differ i…