460 citations · 878 across the 26 of their papers we have counts for
27 papers
Efficient SAGE Estimation via Causal Structure Learning
Christoph Luther, Gunnar König, Moritz Grosse-Wentrup
The Shapley Additive Global Importance (SAGE) value is a theoretically appealing interpretability method that fairly attributes global importance to a model's features. However, it…
Improvement-Focused Causal Recourse (ICR)
Gunnar König, Timo Freiesleben, Moritz Grosse-Wentrup
Algorithmic recourse recommendations, such as Karimi et al.'s (2021) causal recourse (CR), inform stakeholders of how to act to revert unfavourable decisions. However, some actions…
2021 BEETL Competition: Advancing Transfer Learning for Subject Independence & Heterogenous EEG Data Sets
Xiaoxi Wei, A. Aldo Faisal, Moritz Grosse-Wentrup +18
Transfer learning and meta-learning offer some of the most promising avenues to unlock the scalability of healthcare and consumer technologies driven by biosignal data. This is bec…
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…
Decomposition of Global Feature Importance into Direct and Associative Components (DEDACT)
Gunnar König, Timo Freiesleben, Bernd Bischl +2
Global model-agnostic feature importance measures either quantify whether features are directly used for a model's predictions (direct importance) or whether they contain predictio…
A Distance Covariance-based Kernel for Nonlinear Causal Clustering in Heterogeneous Populations
Alex Markham, Richeek Das, Moritz Grosse-Wentrup
We consider the problem of causal structure learning in the setting of heterogeneous populations, i.e., populations in which a single causal structure does not adequately represent…