@article{jullum2025shapr,
  title={shapr: Explaining Machine Learning Models with Conditional Shapley Values in {R} and Python},
  author={Jullum, Martin and Olsen, Lars Henry Berge and Lachmann, Jon and Redelmeier, Annabelle},
  journal={arXiv preprint arXiv:2504.01842},
  year={2025},
  url={https://arxiv.org/abs/2504.01842}
}


@inproceedings{redelmeier2020explaining,
  title={Explaining predictive models with mixed features using Shapley values and conditional inference trees},
  author={Redelmeier, Annabelle and Jullum, Martin and Aas, Kjersti},
  booktitle={International Cross-Domain Conference for Machine Learning and Knowledge Extraction},
  pages={117--137},
  year={2020},
  organization={Springer}
}

@article{aas2019explaining,
  title={Explaining individual predictions when features are dependent: More accurate approximations to Shapley values},
  author={Aas, Kjersti and Jullum, Martin and L{\o}land, Anders},
  journal={{Artificial Intelligence}},
  volume={298},
  year={2021},
  doi={10.1016/j.artint.2021.103502}
}

@inproceedings{lundberg2017unified,
  title={A unified approach to interpreting model predictions},
  author={Lundberg, Scott M and Lee, Su-In},
  booktitle={Advances in Neural Information Processing Systems},
  pages={4765--4774},
  year={2017}
}

@article{kononenko2010efficient,
  title={An efficient explanation of individual classifications using game theory},
  author={{\v{S}}trumbelj, Erik and Kononenko, Igor},
  journal={Journal of Machine Learning Research},
  volume={11},
  number={Jan},
  pages={1--18},
  year={2010}
}

@article{vstrumbelj2014explaining,
  title={Explaining prediction models and individual predictions with feature contributions},
  author={{\v{S}}trumbelj, Erik and Kononenko, Igor},
  journal={Knowledge and information systems},
  volume={41},
  number={3},
  pages={647--665},
  year={2014},
  publisher={Springer}
}

@article{lundberg2018consistent,
  title={Consistent individualized feature attribution for tree ensembles},
  author={Lundberg, Scott M and Erion, Gabriel G and Lee, Su-In},
  journal={arXiv preprint arXiv:1802.03888},
  year={2018}
}

@article{hurvich1998smoothing,
  title={Smoothing parameter selection in nonparametric regression using an improved Akaike information criterion},
  author={Hurvich, Clifford M and Simonoff, Jeffrey S and Tsai, Chih-Ling},
  journal={Journal of the Royal Statistical Society: Series B (Statistical Methodology)},
  volume={60},
  number={2},
  pages={271--293},
  year={1998},
  publisher={Wiley Online Library}
}

@article{Shapley53,
author={Lloyd S. Shapley},
title={{A Value for N-Person Games}},
journal={{Contributions to the Theory of Games}},
volume=2,
year = 1953,
pages ={307--317}
}

@article{sklar1959fonctions,
  title={Fonctions de repartition an dimensions et leurs marges},
  author={Sklar, M},
  journal={Publ. inst. statist. univ. Paris},
  volume={8},
  pages={229--231},
  year={1959}
}

@article{rosenblatt1956,
author = {Murray Rosenblatt},
journal = {{The Annals of Mathematical Statistics}},
pages = {832--837},
title = {{Remarks on Some Nonparametric Estimates of a Density Function}},
volume = 27,
year = 1956
}

@Manual{lime_api,
    title = {lime: Local Interpretable Model-Agnostic Explanations},
    author = {Thomas Lin Pedersen and Michaël Benesty},
    year = {2019},
    note = {R package version 0.5.1},
    url = {https://CRAN.R-project.org/package=lime},
}

@article{hothorn2006unbiased,
	title={Unbiased recursive partitioning: A conditional inference framework},
	author={Hothorn, Torsten and Hornik, Kurt and Zeileis, Achim},
	journal={Journal of Computational and Graphical statistics},
	volume={15},
	number={3},
	pages={651--674},
	year={2006},
	publisher={Taylor \& Francis}
}

@article{partykit_package,
	title = {{partykit}: A Modular Toolkit for Recursive Partytioning in {R}},
	author = {Torsten Hothorn and Achim Zeileis},
	journal = {Journal of Machine Learning Research},
	year = {2015},
	volume = {16},
	pages = {3905-3909},
}

@article{Redelmeier2020ctree,
  title={Explaining predictive models with mixed features using Shapley values and conditional inference trees},
  author={Redelmeier, Annabelle and Jullum, Martin and Aas, Kjersti},
  journal={Submitted},
  year={2020}
}

@inproceedings{jullum2021efficient,
  title={Efficient and simple prediction explanations with groupShapley: A practical perspective},
  author={Jullum, Martin and Redelmeier, Annabelle and Aas, Kjersti},
  year={2021},
  pages={28-43},
  booktitle={Proceedings of the 2nd Italian Workshop on Explainable Artificial Intelligence},
  publisher={CEUR Workshop Proceedings}
}

@article{olsen2022using,
  title={Using Shapley Values and Variational Autoencoders to Explain Predictive Models with Dependent Mixed Features},
  author={Olsen, Lars Henry Berge and Glad, Ingrid Kristine and Jullum, Martin and Aas, Kjersti},
  journal={Journal of Machine Learning Research},
  volume={23},
  number={213},
  pages={1--51},
  year={2022}
}

@article{olsen2024comparative,
  title={A comparative study of methods for estimating model-agnostic Shapley value explanations},
  author={Olsen, Lars Henry Berge and Glad, Ingrid Kristine and Jullum, Martin and Aas, Kjersti},
  journal={Data Mining and Knowledge Discovery},
  pages={1--48},
  year={2024},
  publisher={Springer}
}

@inproceedings{frye2020shapley,
  title={Shapley explainability on the data manifold},
  author={Christopher Frye and Damien de Mijolla and Tom Begley and Laurence Cowton and Megan Stanley and Ilya Feige},
  booktitle={International Conference on Learning Representations},
  year={2021}
}

@article{covert2020understanding,
  title={Understanding global feature contributions with additive importance measures},
  author={Covert, Ian and Lundberg, Scott M and Lee, Su-In},
  journal={Advances in Neural Information Processing Systems},
  volume={33},
  pages={17212--17223},
  year={2020}
}

@inproceedings{kingma2014autoencoding,
  title = {{Auto-Encoding Variational Bayes}},
  author = {Kingma, Diederik P. and Welling, Max},
  booktitle = {2nd International Conference on Learning Representations, {ICLR} 2014, Banff, AB, Canada, April 14-16, 2014, Conference Track Proceedings},
  year = 2014
}

@Manual{torch,
  title = {torch: Tensors and Neural Networks with 'GPU' Acceleration},
  author = {Daniel Falbel and Javier Luraschi},
  year = {2023},
  note = {R package version 0.11.0},
  url = {https://CRAN.R-project.org/package=torch}
}

@Manual{tidymodels,
  title = {Tidymodels: a collection of packages for modeling and machine learning using tidyverse principles.},
  author = {Max Kuhn and Hadley Wickham},
  url = {https://www.tidymodels.org/},
  year = {2020}
}

@article{heskes2020causal,
  title={Causal shapley values: Exploiting causal knowledge to explain individual predictions of complex models},
  author={Heskes, Tom and Sijben, Evi and Bucur, Ioan Gabriel and Claassen, Tom},
  journal={Advances in neural information processing systems},
  volume={33},
  pages={4778--4789},
  year={2020}
}

@article{frye2020asymmetric,
  title={Asymmetric shapley values: incorporating causal knowledge into model-agnostic explainability},
  author={Frye, Christopher and Rowat, Colin and Feige, Ilya},
  journal={Advances in Neural Information Processing Systems},
  volume={33},
  pages={1229--1239},
  year={2020}
}

@inproceedings{covert2021improving,
  title={Improving kernelshap: Practical shapley value estimation using linear regression},
  author={Covert, Ian and Lee, Su-In},
  booktitle={International Conference on Artificial Intelligence and Statistics},
  pages={3457--3465},
  year={2021},
  organization={PMLR}
}


@article{sellereite2019shapr,
  title        = {shapr: An R-package for explaining machine learning models with dependence-aware Shapley values},
  author       = {Sellereite, N. and Jullum, M.},
  journal      = {Journal of Open Source Software},
  year         = {2019},
  volume       = {5},
  number       = {46},
  pages        = {2027},
  doi          = {10.21105/joss.02027}
}

@inproceedings{olsen2025improving,
  title={Improving the Weighting Strategy in KernelSHAP},
  author={Olsen, Lars Henry Berge and Jullum, Martin},
  booktitle={World Conference on Explainable Artificial Intelligence},
  pages={194--218},
  year={2025},
  organization={Springer}
}
