Feature relevance quantification in explainable AI: A causal problem

Feature relevance quantification in explainable AI: A causal problem

29 October 2019

Dominik Janzing

Patrick Blobaum

Papers citing "Feature relevance quantification in explainable AI: A causal problem"

11 / 61 papers shown

Title
Shapley Flow: A Graph-based Approach to Interpreting Model Predictions Jiaxuan Wang Jenna Wiens Scott M. Lundberg FAtt 25 88 0 27 Oct 2020
Interpretable Machine Learning -- A Brief History, State-of-the-Art and Challenges Christoph Molnar Giuseppe Casalicchio B. Bischl AI4TS AI4CE 22 396 0 19 Oct 2020
On the Tractability of SHAP Explanations Mathias Niepert A. Lykov Maximilian Schleich Dan Suciu FAtt TDI 11 260 0 18 Sep 2020
General Pitfalls of Model-Agnostic Interpretation Methods for Machine Learning Models Christoph Molnar Gunnar Konig J. Herbinger Timo Freiesleben Susanne Dandl Christian A. Scholbeck Giuseppe Casalicchio Moritz Grosse-Wentrup B. Bischl FAtt AI4CE 31 135 0 08 Jul 2020
Model Explanations with Differential Privacy Neel Patel Reza Shokri Yair Zick SILM FedML 28 32 0 16 Jun 2020
Explainable AI for a No-Teardown Vehicle Component Cost Estimation: A Top-Down Approach A. Moawad E. Islam Namdoo Kim R. Vijayagopal A. Rousseau Wei Biao Wu 23 5 0 15 Jun 2020
Model-agnostic Feature Importance and Effects with Dependent Features -- A Conditional Subgroup Approach Christoph Molnar Gunnar Konig B. Bischl Giuseppe Casalicchio 31 77 0 08 Jun 2020
Asymmetric Shapley values: incorporating causal knowledge into model-agnostic explainability Christopher Frye C. Rowat Ilya Feige 16 180 0 14 Oct 2019
Technical Report: Partial Dependence through Stratification T. Parr James D. Wilson 11 2 0 15 Jul 2019
Explaining individual predictions when features are dependent: More accurate approximations to Shapley values K. Aas Martin Jullum Anders Løland FAtt TDI 16 606 0 25 Mar 2019
Adversarial examples in the physical world Alexey Kurakin Ian Goodfellow Samy Bengio SILM AAML 326 5,847 0 08 Jul 2016