Visualizing the Feature Importance for Black Box Models. - Giuseppe Casalicchio; Christoph Molnar; Bernd Bischl (ECML/PKDD (1), 2018)

iml: An R package for Interpretable Machine Learning. - Christoph Molnar; Giuseppe Casalicchio; Bernd Bischl (J. Open Source Softw., 2018)

Quantifying Model Complexity via Functional Decomposition for Better Post-hoc Interpretability. - Christoph Molnar; Giuseppe Casalicchio; Bernd Bischl (PKDD/ECML Workshops (1), 2019)

Sampling, Intervention, Prediction, Aggregation: A Generalized Framework for Model-Agnostic Interpretations. - Christian A. Scholbeck; Christoph Molnar; Christian Heumann; Bernd Bischl; Giuseppe Casalicchio (PKDD/ECML Workshops (1), 2019)

Quantifying Interpretability of Arbitrary Machine Learning Models Through Functional Decomposition. - Christoph Molnar; Giuseppe Casalicchio; Bernd Bischl (CoRR, 2019)

Explainable AI Methods - A Brief Overview. - Andreas Holzinger; Anna Saranti; Christoph Molnar; Przemyslaw Biecek; Wojciech Samek (xxAI@ICML, 2020)

General Pitfalls of Model-Agnostic Interpretation Methods for Machine Learning Models. - Christoph Molnar; Gunnar König; Julia Herbinger; Timo Freiesleben; Susanne Dandl; Christian A. Scholbeck; Giuseppe Casalicchio; Moritz Grosse-Wentrup; Bernd Bischl (xxAI@ICML, 2020)

Relative Feature Importance. - Gunnar König; Christoph Molnar; Bernd Bischl; Moritz Grosse-Wentrup (ICPR, 2020)

Interpretable Machine Learning - A Brief History, State-of-the-Art and Challenges. - Christoph Molnar; Giuseppe Casalicchio; Bernd Bischl (PKDD/ECML Workshops, 2020)

Multi-Objective Counterfactual Explanations. - Susanne Dandl; Christoph Molnar; Martin Binder; Bernd Bischl (PPSN (1), 2020)

Pitfalls to Avoid when Interpreting Machine Learning Models. - Christoph Molnar; Gunnar König; Julia Herbinger; Timo Freiesleben; Susanne Dandl; Christian A. Scholbeck; Giuseppe Casalicchio; Moritz Grosse-Wentrup; Bernd Bischl (CoRR, 2020)

Relating the Partial Dependence Plot and Permutation Feature Importance to the Data Generating Process. - Christoph Molnar; Timo Freiesleben; Gunnar König; Giuseppe Casalicchio; Marvin N. Wright; Bernd Bischl (CoRR, 2021)

Model-agnostic interpretable machine learning. - Christoph Molnar (2022)

Relating the Partial Dependence Plot and Permutation Feature Importance to the Data Generating Process. - Christoph Molnar; Timo Freiesleben; Gunnar König; Julia Herbinger; Tim Reisinger; Giuseppe Casalicchio; Marvin N. Wright; Bernd Bischl (xAI (1), 2023)

Model-agnostic feature importance and effects with dependent features: a conditional subgroup approach. - Christoph Molnar; Gunnar König; Bernd Bischl; Giuseppe Casalicchio (Data Min. Knowl. Discov., 2024)

Marginal effects for non-linear prediction functions. - Christian A. Scholbeck; Giuseppe Casalicchio; Christoph Molnar; Bernd Bischl; Christian Heumann (Data Min. Knowl. Discov., 2024)

Scientific Inference with Interpretable Machine Learning: Analyzing Models to Learn About Real-World Phenomena. - Timo Freiesleben; Gunnar König; Christoph Molnar; Álvaro Tejero-Cantero (Minds Mach., 2024)

Correction: Marginal effects for non-linear prediction functions. - Christian A. Scholbeck; Giuseppe Casalicchio; Christoph Molnar; Bernd Bischl; Christian Heumann (Data Min. Knowl. Discov., 2024)