112 citations · 164 across the 6 of their papers we have counts for
Showing 2024Show all
2 papers · 1 filter
stat.ML2024
CountARFactuals -- Generating plausible model-agnostic counterfactual explanations with adversarial random forests
Susanne Dandl, Kristin Blesch, Timo Freiesleben +4
Counterfactual explanations elucidate algorithmic decisions by pointing to scenarios that would have led to an alternative, desired outcome. Giving insight into the model's behavio…
stat.ML2024★ 38 cited
A Guide to Feature Importance Methods for Scientific Inference
Fiona Katharina Ewald, Ludwig Bothmann, Marvin N. Wright +3
While machine learning (ML) models are increasingly used due to their high predictive power, their use in understanding the data-generating process (DGP) is limited. Understanding…