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)

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)

mcboost: Multi-Calibration Boosting for R. - Florian Pfisterer; Christoph Kern 001; Susanne Dandl; Matthew Sun; Michael P. Kim; Bernd Bischl (J. Open Source Softw., 2021)

mcboost: Multi-Calibration Boosting for R. - Florian Pfisterer; Christoph Kern 001; Susanne Dandl; Mathew Sun; Michael P. Kim; Bernd Bischl (Zenodo, 2021)

Multi-objective counterfactual fairness. - Susanne Dandl; Florian Pfisterer; Bernd Bischl (GECCO Companion, 2022)

Causal Fair Machine Learning via Rank-Preserving Interventional Distributions. - Ludwig Bothmann; Susanne Dandl; Michael Schomaker (AEQUITAS@ECAI, 2023)

Leveraging Model-Based Trees as Interpretable Surrogate Models for Model Distillation. - Julia Herbinger; Susanne Dandl; Fiona Katharina Ewald; Sofia Loibl; Giuseppe Casalicchio (ECAI Workshops (1), 2023)

Interpretable Regional Descriptors: Hyperbox-Based Local Explanations. - Susanne Dandl; Giuseppe Casalicchio; Bernd Bischl; Ludwig Bothmann (ECML/PKDD (3), 2023)

counterfactuals: An R Package for Counterfactual Explanation Methods. - Susanne Dandl; Andreas Hofheinz; Martin Binder; Bernd Bischl; Giuseppe Casalicchio (CoRR, 2023)

Causality concepts in machine learning: heterogeneous treatment effect estimation with machine learning & model interpretation with counterfactual and semi-factual explanations. - Susanne Dandl (2023)

mlr3summary: Concise and interpretable summaries for machine learning models. - Susanne Dandl; Marc Becker; Bernd Bischl; Giuseppe Casalicchio; Ludwig Bothmann (xAI (Late-breaking Work, Demos, Doctoral Consortium), 2024)

CountARFactuals - Generating Plausible Model-Agnostic Counterfactual Explanations with Adversarial Random Forests. - Susanne Dandl; Kristin Blesch; Timo Freiesleben; Gunnar König; Jan Kapar; Bernd Bischl; Marvin N. Wright (xAI (3), 2024)

Privilege Scores. - Ludwig Bothmann; Philip A. Boustani; José M. Álvarez 002; Giuseppe Casalicchio; Bernd Bischl; Susanne Dandl (CoRR, 2025)