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)

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)

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)

Algorithm-Agnostic Interpretations for Clustering. - Christian A. Scholbeck; Henri Funk; Giuseppe Casalicchio (CoRR, 2022)

Algorithm-Agnostic Feature Attributions for Clustering. - Christian A. Scholbeck; Henri Funk; Giuseppe Casalicchio (xAI (1), 2023)

Position Paper: Bridging the Gap Between Machine Learning and Sensitivity Analysis. - Christian A. Scholbeck; Julia Moosbauer; Giuseppe Casalicchio; Hoshin Gupta; Bernd Bischl; Christian Heumann (CoRR, 2023)

Bridging gaps in interpretable machine learning: sensitivity analysis, marginal effects, and cluster explanations. - Christian A. Scholbeck (2024)

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

fmeffects: An R Package for Forward Marginal Effects. - Holger Löwe; Christian A. Scholbeck; Christian Heumann; Bernd Bischl; Giuseppe Casalicchio (R J., 2025)