source/tex/references.bib

@inproceedings{langley00,
 author    = {P. Langley},
 title     = {Crafting Papers on Machine Learning},
 year      = {2000},
 pages     = {1207--1216},
 editor    = {Pat Langley},
 booktitle     = {Proceedings of the 17th International Conference
              on Machine Learning (ICML 2000)},
 address   = {Stanford, CA},
 publisher = {Morgan Kaufmann}
}

@TechReport{mitchell80,
  author = 	 "T. M. Mitchell",
  title = 	 "The Need for Biases in Learning Generalizations",
  institution =  "Computer Science Department, Rutgers University",
  year = 	 "1980",
  address =	 "New Brunswick, MA",
}

@phdthesis{kearns89,
  author = {M. J. Kearns},
  title =  {Computational Complexity of Machine Learning},
  school = {Department of Computer Science, Harvard University},
  year =   {1989}
}

@Book{MachineLearningI,
  editor = 	 "R. S. Michalski and J. G. Carbonell and T.
		  M. Mitchell",
  title = 	 "Machine Learning: An Artificial Intelligence
		  Approach, Vol. I",
  publisher = 	 "Tioga",
  year = 	 "1983",
  address =	 "Palo Alto, CA"
}

@Book{DudaHart2nd,
  author =       "R. O. Duda and P. E. Hart and D. G. Stork",
  title =        "Pattern Classification",
  publisher =    "John Wiley and Sons",
  edition =      "2nd",
  year =         "2000"
}

@misc{anonymous,
  title= {Suppressed for Anonymity},
  author= {Author, N. N.},
  year= {2021}
}

@InCollection{Newell81,
  author =       "A. Newell and P. S. Rosenbloom",
  title =        "Mechanisms of Skill Acquisition and the Law of
                  Practice", 
  booktitle =    "Cognitive Skills and Their Acquisition",
  pages =        "1--51",
  publisher =    "Lawrence Erlbaum Associates, Inc.",
  year =         "1981",
  editor =       "J. R. Anderson",
  chapter =      "1",
  address =      "Hillsdale, NJ"
}


@Article{Samuel59,
  author = 	 "A. L. Samuel",
  title = 	 "Some Studies in Machine Learning Using the Game of
		  Checkers",
  journal =	 "IBM Journal of Research and Development",
  year =	 "1959",
  volume =	 "3",
  number =	 "3",
  pages =	 "211--229"
}


% do-SHAP bibliography

@misc{euro2016gdpr,
  author = {{European Commission}},
  publisher = {European Commission},
  title = {Regulation ({EU}) 2016/679 of the {European} {Parliament} and of the {Council} of 27 {April} 2016 on the protection of natural persons with regard to the processing of personal data and on the free movement of such data, and repealing {Directive} 95/46/{EC} ({General} {Data} {Protection} {Regulation})},
  url = {https://eur-lex.europa.eu/eli/reg/2016/679/oj},
  year = 2016
}

@article{angwin2016propublica,
  added-at = {2020-09-14T11:52:21.000+0200},
  author = {Angwin, Julia and Larson, Jeff and Mattu, Surya and Kirchner, Lauren},
  biburl = {https://www.bibsonomy.org/bibtex/23d537ed0185eb7820ed6769aca10acb4/wanlo},
  interhash = {e9260c2f8fdd08ef1a34f7a3a243b0ff},
  intrahash = {3d537ed0185eb7820ed6769aca10acb4},
  journal = {Propublica},
  keywords = {background},
  timestamp = {2020-09-14T11:52:21.000+0200},
  title = {{Machine Bias}},
  month = {May},
  year = 2016
}

@inproceedings{neuhaus2023spurious,
  title={Spurious features everywhere - large-scale detection of harmful spurious features in {I}mage{N}et},
  author={Neuhaus, Yannic and Augustin, Maximilian and Boreiko, Valentyn and Hein, Matthias},
  booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision},
  pages={20235--20246},
  year={2023}
}

@inproceedings{szegedy2014intriguing,
  title={Intriguing properties of neural networks},
  author={Szegedy, Christian and Zaremba, Wojciech and Sutskever, Ilya and Bruna, Joan and Erhan, Dumitru and Goodfellow, Ian and Fergus, Rob},
  booktitle={2nd International Conference on Learning Representations, ICLR},
  year={2014}
}

@article{strumbelj2014explaining,
  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},
  pages={647--665},
  year={2014},
  publisher={Springer}
}

@article{chen2023shap_survey,
  title={Algorithms to estimate {S}hapley value feature attributions},
  author={Chen, Hugh and Covert, Ian C and Lundberg, Scott M and Lee, Su-In},
  journal={Nature Machine Intelligence},
  pages={1--12},
  year={2023},
  publisher={Nature Publishing Group UK London}
}

@article{frye2020asymmetric_shap,
  title={Asymmetric {S}hapley values: incorporating causal knowledge into model-agnostic explainability},
  author={Frye, Christopher and Rowat, Colin and Feige, Ilya},
  journal={Advances in Neural Information Processing Systems (NeurIPS)},
  volume={33},
  pages={1229--1239},
  year={2020}
}

@article{heskes2020causal_shap,
  title={Causal {S}hapley 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 (NeurIPS)},
  volume={33},
  pages={4778--4789},
  year={2020}
}

@inproceedings{jung2022do_shap,
  title={On measuring causal contributions via do-interventions},
  author={Jung, Yonghan and Kasiviswanathan, Shiva and Tian, Jin and Janzing, Dominik and Bl{\"o}baum, Patrick and Bareinboim, Elias},
  booktitle={International Conference on Machine Learning},
  pages={10476--10501},
  year={2022},
  organization={PMLR}
}

@article{lundberg2017shap,
  title={A unified approach to interpreting model predictions},
  author={Lundberg, Scott M and Lee, Su-In},
  journal={Advances in neural information processing systems},
  volume={30},
  year={2017}
}

@inproceedings{janzing2020feature,
  title={Feature relevance quantification in explainable {AI}: A causal problem},
  author={Janzing, Dominik and Minorics, Lenon and Bl{\"o}baum, Patrick},
  booktitle={International Conference on artificial intelligence and statistics},
  pages={2907--2916},
  year={2020},
  organization={PMLR}
}

@article{lauritzen2002chaingraph,
  title={Chain graph models and their causal interpretations},
  author={Lauritzen, Steffen L and Richardson, Thomas S},
  journal={Journal of the Royal Statistical Society Series B: Statistical Methodology},
  volume={64},
  number={3},
  pages={321--348},
  year={2002},
  publisher={Oxford University Press}
}

@article{zhang2021expl_survey,
  title={A survey on neural network interpretability},
  author={Zhang, Yu and Ti{\v{n}}o, Peter and Leonardis, Ale{\v{s}} and Tang, Ke},
  journal={IEEE Transactions on Emerging Topics in Computational Intelligence},
  volume={5},
  number={5},
  pages={726--742},
  year={2021},
  publisher={IEEE}
}

@inproceedings{kocaoglu2017causalgan,
    title={Causal{GAN}: learning Causal Implicit Generative Models with Adversarial Training},
    author={Murat Kocaoglu and Christopher Snyder and Alexandros G. Dimakis and Sriram Vishwanath},
    booktitle={Proceedings of the 6th International Conference on Learning Representations (ICLR)},
    year={2018},
    address="Vancouver, Canada"
}

@article{goodfellow2020gan,
  title={Generative {A}dversarial {N}etworks},
  author={Goodfellow, Ian and Pouget-Abadie, Jean and Mirza, Mehdi and Xu, Bing and Warde-Farley, David and Ozair, Sherjil and Courville, Aaron and Bengio, Yoshua},
  journal={Communications of the ACM},
  volume={63},
  number={11},
  pages={139--144},
  year={2020},
  publisher={ACM New York, NY, USA}
}

@inproceedings{parafita2019dcn,
  title={Explaining visual models by causal attribution},
  author={Parafita, {\'A}lvaro and Vitri{\`a}, Jordi},
  booktitle={2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW)},
  pages={4167--4175},
  year={2019},
  organization={IEEE},
  address="Seoul, Korea"
}

@article{papamakarios2019normalizing,
  title={Normalizing {Flows} for probabilistic modeling and inference},
  author={Papamakarios, George and Nalisnick, Eric and Rezende, Danilo Jimenez and Mohamed, Shakir and Lakshminarayanan, Balaji},
  journal={Journal of Machine Learning Research},
  volume={22},
  number={57},
  year={2021}
}

@inproceedings{pawlowski2020deep,
  title={Deep {Structural} {Causal} {Models} for Tractable Counterfactual Inference},
  author={Pawlowski, Nick and Coelho de Castro, Daniel and Glocker, Ben},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  volume={33},
  year={2020}
}

@article{parafita2020dcg,
  title={Causal Inference with {Deep} {Causal} {Graphs}},
  author={Parafita, {\'A}lvaro and Vitri{\`a}, Jordi},
  journal={arXiv preprint arXiv:2006.08380},
  year={2020}
}

@inproceedings{sanchez2022diffusion,
  title={Diffusion Causal Models for Counterfactual Estimation},
  author={Sanchez, Pedro and Tsaftaris, Sotirios A},
  booktitle={Conference on Causal Learning and Reasoning},
  pages={647--668},
  year={2022},
  organization={PMLR}
}

@article{chao2023interventional,
  title={Interventional and counterfactual inference with diffusion models},
  author={Chao, Patrick and Bl{\"o}baum, Patrick and Kasiviswanathan, Shiva Prasad},
  journal={arXiv preprint arXiv:2302.00860},
  year={2023}
}

@article{ho2020denoising,
  title={Denoising diffusion probabilistic models},
  author={Ho, Jonathan and Jain, Ajay and Abbeel, Pieter},
  journal={Advances in Neural Information Processing Systems (NeurIPS)},
  volume={33},
  pages={6840--6851},
  year={2020}
}

@inproceedings{xia2021neural,
  title={The causal-neural connection: expressiveness, learnability, and inference},
  author={Xia, Kevin and Lee, Kai-Zhan and Bengio, Yoshua and Bareinboim, Elias},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  volume={34},
  year={2021},
  pages={10823--10836}
}

@inproceedings{xia2023neural,
  title={Neural causal models for counterfactual identification and estimation},
  author={Xia, Kevin Muyuan and Pan, Yushu and Bareinboim, Elias},
  booktitle={The Eleventh International Conference on Learning Representations},
  year={2023}
}

@inproceedings{sanchez2021vaca,
  title={{VACA}: designing {Variational} {Graph} {Autoencoders} for causal queries},
  author={S{\'a}nchez-Mart{\i}n, Pablo and Rateike, Miriam and Valera, Isabel},
  year={2022},
  booktitle={Proceedings of the 36th AAAI Conference on Artificial Intelligence},
  volume={36}
}

@article{zhou2020gnn,
  title={Graph neural networks: A review of methods and applications},
  author={Zhou, Jie and Cui, Ganqu and Hu, Shengding and Zhang, Zhengyan and Yang, Cheng and Liu, Zhiyuan and Wang, Lifeng and Li, Changcheng and Sun, Maosong},
  journal={AI open},
  volume={1},
  pages={57--81},
  year={2020},
  publisher={Elsevier}
}

@article{javaloy2023causal,
  title={Causal normalizing flows: from theory to practice},
  author={Javaloy, Adri{\'a}n and S{\'a}nchez-Mart{\'\i}n, Pablo and Valera, Isabel},
  journal={Advances in Neural Information Processing Systems},
  volume={36},
  year={2024}
}

@article{parafita2022dcg,
  title={Estimand-Agnostic Causal Query Estimation With {Deep} {Causal} {Graphs}},
  author={Parafita, {\'A}lvaro and Vitri{\`a}, Jordi},
  journal={IEEE Access},
  volume={10},
  pages={71370--71386},
  year={2022},
  publisher={IEEE}
}

@incollection{shapley1953shap,
  title={A Value for n-Person Games},
  author={Shapley, LS},
  booktitle={Contributions to the Theory of Games (AM-28), Volume II},
  pages={307--317},
  year={1953},
  publisher={Princeton University Press}
}

@article{scholkopf2021towardCRL,
  title={Toward causal representation learning},
  author={Sch{\"o}lkopf, Bernhard and Locatello, Francesco and Bauer, Stefan and Ke, Nan Rosemary and Kalchbrenner, Nal and Goyal, Anirudh and Bengio, Yoshua},
  journal={Proceedings of the IEEE},
  volume={109},
  number={5},
  pages={612--634},
  year={2021},
  publisher={IEEE}
}

@inproceedings{lee2020projection,
  title={Causal effect identifiability under partial-observability},
  author={Lee, Sanghack and Bareinboim, Elias},
  booktitle={International Conference on Machine Learning},
  pages={5692--5701},
  year={2020},
  organization={PMLR}
}

@book{mann1960approxshap,
  title={Values of large games, IV: Evaluating the electoral college by Montecarlo techniques},
  author={Mann, Irwin and Shapley, Lloyd S},
  year={1960},
  publisher={Rand Corporation}
}  % approximate SHAP with permutations

@inproceedings{durkan2019nsf,
  title={Neural {Spline} {Flows}},
  author={Durkan, Conor and Bekasov, Artur and Murray, Iain and Papamakarios, George},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  volume={32},
  pages={7511--7522},
  year={2019},
  address="Vancouver, Canada"
}

@article{clevert2015elu,
  title={Fast and accurate deep network learning by exponential linear units (elus)},
  author={Clevert, Djork-Arn{\'e} and Unterthiner, Thomas and Hochreiter, Sepp},
  journal={arXiv preprint arXiv:1511.07289},
  year={2015}
}

@inproceedings{
loshchilov2018decoupled,
title={Decoupled Weight Decay Regularization},
author={Ilya Loshchilov and Frank Hutter},
booktitle={International Conference on Learning Representations},
year={2019},
}

@article{lundberg2018treeshap,
  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}
}

@misc{diabetesUCI,
  author       = {CDC},
  title        = {{CDC} {D}iabetes {H}ealth {I}ndicators},
  year         = {2015},
  howpublished = {UCI Machine Learning Repository},
  note         = {Preprocessed dataset downloaded from {DOI}: https://doi.org/10.24432/C53919}
}

@Misc{eu2023AIact,
author = {{Council of the EU}},
title = {Artificial {I}ntelligence {A}ct: {C}ouncil and {P}arliament strike a deal on the first rules for {AI} in the world [{P}ress release]},
howpublished = {https://www.consilium.europa.eu/en/press/press-releases/2023/12/09/artificial-intelligence-act-council-and-parliament-strike-a-deal-on-the-first-worldwide-rules-for-ai/},
month = {December},
year = {2023}
}

@article{rubin2005potentialoutcomes,
  title={Causal inference using potential outcomes: design, modeling, decisions},
  author={Rubin, Donald B},
  journal={Journal of the American Statistical Association},
  volume={100},
  number={469},
  pages={322--331},
  year={2005},
  publisher={Taylor \& Francis}
}

@inproceedings{shpitser2006interventional,
  title={Identification of joint interventional distributions in recursive semi-{Markovian} causal models},
  author={Shpitser, Ilya and Pearl, Judea},
  booktitle={Proceedings of 21st National Conference on Artificial Intelligence (AAAI)},
  address="Boston, MA, USA",
  pages={1219--1226},
  year={2006}
}

% Teal thinks this is hallucinated
%inproceedings{shpitser2006interventionalconditional,
%  title={Identification of conditional interventional distributions},
%  author={Shpitser, Ilya and Pearl, Judea},
%  booktitle={Proceedings of the 22th Conference on Uncertainty in Artificial Intelligence (UAI)},
%  pages={437--444},
%  address="Cambridge, MA, USA",
%  year={2006}
%}

@inproceedings{shpitser2007counterfactuals,
  title={What counterfactuals can be tested},
  author={Shpitser, Ilya and Pearl, Judea},
  booktitle={Proceedings of the 23th Conference on Uncertainty in Artificial Intelligence (UAI)},
  address="Vancouver, Canada",
  pages={352--359},
  year={2007}
}

@article{tikka2017identifiability,
  title={Identifying Causal Effects with the {R} Package causaleffect},
  author={Tikka, Santtu and Karvanen, Juha},
  journal={Journal of Statistical Software},
  volume={76},
  number={12},
  year={2017},
  pages={1--30},
  publisher={Foundation for Open Access Statistics}
}

@inproceedings{tian2002testable,
  title={On the testable implications of causal models with hidden variables},
  author={Tian, Jin and Pearl, Judea},
  booktitle={Proceedings of the 18th Conference on Uncertainty in Artificial Intelligence (UAI)},
  pages={519--527},
  year={2002},
  address="Edmonton, Canada"
}

@inproceedings{johansson2016bnn,
  title={Learning representations for counterfactual inference},
  author={Johansson, Fredrik and Shalit, Uri and Sontag, David},
  booktitle={Proceedings of the 33rd International Conference on Machine Learning (ICML)},
  pages={3020--3029},
  year={2016},
  address="New York, NY, USA",
  organization={PMLR}
}

@inproceedings{shalit2017tarnet,
  title={Estimating individual treatment effect: generalization bounds and algorithms},
  author={Shalit, Uri and Johansson, Fredrik D and Sontag, David},
  booktitle={Proceedings of the 34th International Conference on Machine Learning (ICML)},
  address="Sydney, Australia",
  pages={3076--3085},
  year={2017},
  organization={PMLR}
}

@inproceedings{yao2019ace,
  title={{ACE}: adaptively similarity-preserved representation learning for individual treatment effect estimation},
  author={Yao, Liuyi and Li, Sheng and Li, Yaliang and Huai, Mengdi and Gao, Jing and Zhang, Aidong},
  booktitle={Proceedings of the 2019 IEEE International Conference on Data Mining (ICDM)},
  address="Beijing, China",
  pages={1432--1437},
  year={2019},
  organization={IEEE}
}

@article{li2021causal,
  title={Causal Optimal Transport for Treatment Effect Estimation},
  author={Li, Qian and Wang, Zhichao and Liu, Shaowu and Li, Gang and Xu, Guandong},
  journal={IEEE Transactions on Neural Networks and Learning Systems},
  year={2021},
  publisher={IEEE}
}

@inproceedings{yao2021sci,
  title={{SCI}: subspace Learning Based Counterfactual Inference for Individual Treatment Effect Estimation},
  author={Yao, Liuyi and Li, Yaliang and Li, Sheng and Huai, Mengdi and Gao, Jing and Zhang, Aidong},
  booktitle={Proceedings of the 30th ACM International Conference on Information \& Knowledge Management (CIKM)},
  pages={3583--3587},
  year={2021},
  volume={30},
  address="Gold Coast, Australia"
}

@article{wachter2017counterfactual,
  title={Counterfactual explanations without opening the black box: automated decisions and the {GDPR}},
  author={Wachter, Sandra and Mittelstadt, Brent and Russell, Chris},
  journal={Harvard Journal of Law \& Technology},
  volume={31},
  pages={841},
  year={2017}
}

@inproceedings{hendricks2018generating,
  title={Generating Counterfactual Explanations with {Natural} {Language}},
  author={Hendricks, Lisa Anne and Hu, Ronghang and Darrell, Trevor and Akata, Zeynep},
  booktitle={ICML Workshop on Human Interpretability in Machine Learning},
  pages={95--98},
  year={2018},
  organization={PMLR},
  address="Stockholm, Sweden"
}

@inproceedings{goyal2019counterfactual,
  title={Counterfactual visual explanations},
  author={Goyal, Yash and Wu, Ziyan and Ernst, Jan and Batra, Dhruv and Parikh, Devi and Lee, Stefan},
  booktitle={Proceedings of the 36th International Conference on Machine Learning (ICML)},
  pages={2376--2384},
  year={2019},
  organization={PMLR},
  address="Long Beach, CA, USA"
}

@article{guidotti2019factual,
  title={Factual and counterfactual explanations for black box decision making},
  author={Guidotti, Riccardo and Monreale, Anna and Giannotti, Fosca and Pedreschi, Dino and Ruggieri, Salvatore and Turini, Franco},
  journal={IEEE Intelligent Systems},
  volume={34},
  number={6},
  pages={14--23},
  year={2019},
  publisher={IEEE}
}

@inproceedings{mothilal2020explaining,
  title={Explaining {Machine} {Learning} classifiers through diverse counterfactual explanations},
  author={Mothilal, Ramaravind K and Sharma, Amit and Tan, Chenhao},
  booktitle={Proceedings of the 3rd ACM Conference on Fairness, Accountability, and Transparency (FAT*)},
  pages={607--617},
  year={2020},
  address="Barcelona, Spain"
}

@inproceedings{ancona2018towards,
  title = {Towards better understanding of gradient-based attribution methods for {Deep} {Neural} {Networks}},
  booktitle = {Proceedings of the 6th {International} {Conference} on {Learning} {Representations} (ICLR)},
  year = 2018,
  author = {Ancona, Marco and Ceolini, Enea and Oztireli, Cengiz and Gross, Markus}
}

@inproceedings{ribeiro2016lime,
  title={"{Why} should {I} trust you?" {Explaining} the predictions of any classifier},
  author={Ribeiro, Marco Tulio and Singh, Sameer and Guestrin, Carlos},
  booktitle={Proceedings of the 22nd ACM International Conference on Knowledge Discovery and Data Mining (SIGKDD)},
  pages={1135--1144},
  year={2016},
  address="San Francisco, CA, USA"
}

@inproceedings{spirtes2016discovery,
  title={Causal discovery and inference: concepts and recent methodological advances},
  author={Spirtes, Peter and Zhang, Kun},
  booktitle={Applied informatics},
  volume={3},
  year={2016},
  organization={SpringerOpen}
}

@article{verma2020cfexplanations,
  title={Counterfactual explanations for machine learning: a review},
  author={Verma, Sahil and Dickerson, John and Hines, Keegan},
  journal={arXiv preprint arXiv:2010.10596},
  year={2020}
}

@inproceedings{kusner2017cffairness,
  title={Counterfactual fairness},
  author={Kusner, Matt J and Loftus, Joshua and Russell, Chris and Silva, Ricardo},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  volume={30},
  year={2017},
  address="Long Beach, CA, USA"
}

@book{pearl2018why,
  title={The book of why: the new science of cause and effect},
  author={Pearl, Judea and Mackenzie, Dana},
  year={2019},
  publisher={Penguin Books}
}


// Desiderata

@article{wright1921correlation,
  title={Correlation and causation},
  author={Wright, Sewall},
  journal={Journal of Agricultural Research},
  year={1921}
}

@inproceedings{louizos2017cevae,
  title={Causal effect inference with deep latent-variable models},
  author={Louizos, Christos and Shalit, Uri and Mooij, Joris M and Sontag, David and Zemel, Richard and Welling, Max},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  volume={30},
  year={2017},
  address="Long Beach, CA, USA"
}

@inproceedings{wehenkel2021graphical,
  title={Graphical {Normalizing} {Flows}},
  author={Wehenkel, Antoine and Louppe, Gilles},
  booktitle={Proceedings of the 24th International Conference on Artificial Intelligence and Statistics (AIStats)},
  pages={37--45},
  year={2021},
  organization={PMLR}
}

@inproceedings{kendall2017uncertainty,
 author = {Kendall, Alex and Gal, Yarin},
 booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
 editor = {I. Guyon and U. Von Luxburg and S. Bengio and H. Wallach and R. Fergus and S. Vishwanathan and R. Garnett},
 pages = {},
 publisher = {Curran Associates, Inc.},
 title = {What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?},
 volume = {30},
 year = {2017}
}



// Own contributions:

@article{parafita2021ccia,
  title={{Deep} {Causal} {Graphs} for Causal Inference, Black-Box Explainability and Fairness},
  author={Parafita, {\'A}lvaro and Vitri{\`a}, Jordi},
  journal={Artificial Intelligence Research and Development},
  volume={339},
  pages={415--424},
  year={2021}
}

@article{parafita2022application,
  title={A unified framework for Causal Analysis, Explainability and Fairness},
  author={Parafita, {\'A}lvaro and Vitri{\`a}, Jordi},
  journal={Submitted}
}


@article{pedemonte2021identifiability,
  title={Algorithmic Causal Effect Identification with causaleffect},
  author={Pedemonte, Mart{\'\i} and Vitri{\`a}, Jordi and Parafita, {\'A}lvaro},
  journal={arXiv preprint arXiv:2107.04632},
  year={2021}
}


// Distributional Causal Graphs:

@misc{3dshapes18,
  title={{3D} {Shapes} {Dataset}},
  author={Burgess, Chris and Kim, Hyunjik},
  howpublished={https://github.com/deepmind/3dshapes-dataset/},
  year={2018}
}

@inproceedings{sundararajan2017axiomatic,
  title={Axiomatic attribution for deep networks},
  author={Sundararajan, Mukund and Taly, Ankur and Yan, Qiqi},
  booktitle={Proceedings of the 34th International Conference on Machine Learning (ICML)},
  pages={3319--3328},
  year={2017},
  organization={PMLR},
  address="Sydney, Australia"
}

@inproceedings{zeiler_visualizing_2014,
    title = {Visualizing and Understanding {Convolutional} {Networks}},
    isbn = {978-3-319-10590-1},
    booktitle = {Proceedings of the 13th European Conference on Computer Vision (ECCV)},
    author = {Zeiler, Matthew D. and Fergus, Rob},
    year = {2014},
    pages = {818--833},
    address="Zurich, Switzerland"
}

@inproceedings{ancona_towards_2018,
    title = {Towards better understanding of gradient-based attribution methods for {Deep} {Neural} {Networks}},
    copyright = {http://rightsstatements.org/page/InC-NC/1.0/},
    url = {https://www.research-collection.ethz.ch/handle/20.500.11850/249929},
    doi = {10.3929/ethz-b-000249929},
    booktitle = {Proceedings of the 6th {International} {Conference} on {Learning} {Representations} (ICLR)},
    year = 2018,
    author = {Ancona, Marco and Ceolini, Enea and Oztireli, Cengiz and Gross, Markus},
    address="Vancouver, Canada"
}

@inproceedings{chang_explaining_2018,
    title={Explaining Image Classifiers by Counterfactual Generation},
    author={Chun-Hao Chang and Elliot Creager and Anna Goldenberg and David Duvenaud},
    booktitle={Proceedings of the 7th International Conference on Learning Representations (ICLR)},
    year = 2019,
    url={https://openreview.net/forum?id=B1MXz20cYQ},
    address="New Orleans, LA, USA"
}

@incollection{lample2017fader,
    title = {Fader {Networks}: Manipulating Images by Sliding Attributes},
    shorttitle = {Fader {Networks}},
    booktitle = {Advances in {Neural} {Information} {Processing} {Systems} (NeurIPS)},
    volume={30},
    author = {Lample, Guillaume and Zeghidour, Neil and Usunier, Nicolas and Bordes, Antoine and Denoyer, Ludovic and Ranzato, Marc'Aurelio},
    year = {2017},
    pages = {5967--5976},
    address="Long Beach, CA, USA"
}

@article{he2019attgan,
    title = {{AttGAN}: facial Attribute Editing by Only Changing What You Want},
    issn = {1057-7149},
    shorttitle = {{AttGAN}},
    doi = {10.1109/TIP.2019.2916751},
    journal = {IEEE Transactions on Image Processing},
    author = {He, Z. and Zuo, W. and Kan, M. and Shan, S. and Shan, S. and Chen, X.},
    year = {2019}
}


// DCU

@misc{maddison2017gumbelabduction, 
  title={Gumbel Machinery}, 
  howpublished = "\url{https://cmaddis.github.io/gumbel-machinery}",
  author={Maddison, Chris J and Tarlow, Danny}, 
  year={2017}, month={Jan}
}

@inproceedings{maddison2014sampling,
  title={A* sampling},
  author={Maddison, Chris J and Tarlow, Daniel and Minka, Tom},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  volume={27},
  year={2014},
  address="Montr\'eal, Canada"
}

@inproceedings{kingma2013vae,
  title={{Auto-encoding variational Bayes}},
  author={Diederik P. Kingma and Max Welling},
  booktitle={Proceedings of the 2nd International Conference on Learning Representations (ICLR)},
  year={2014},
  address="Banff, Canada"
}

@inproceedings{brando2019modelling,
  title={Modelling heterogeneous distributions with an Uncountable Mixture of {Asymmetric} {Laplacians}},
  author={Brando, Axel and Rodriguez, Jose A and Vitria, Jordi and Rubio Mu{\~n}oz, Alberto},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  volume={32},
  year={2019},
  address="Vancouver, Canada"
}

@inproceedings{papamakarios2017made,
  title={Masked autoregressive flow for density estimation},
  author={Papamakarios, George and Pavlakou, Theo and Murray, Iain},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  volume={30},
  year={2017}
}

@article{huijben2022gumbel,
  title={A Review of the {Gumbel}-max Trick and its Extensions for Discrete Stochasticity in {Machine} {Learning}},
  author={Huijben, Iris AM and Kool, Wouter and Paulus, Max Benedikt and Van Sloun, Ruud JG},
  journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
  year={2022},
  publisher={IEEE}
}


% Experiments

@incollection{pytorch,
    title = {PyTorch: An Imperative Style, High-Performance {Deep} {Learning} {Library}},
    author = {Paszke, Adam and Gross, Sam and Massa, Francisco and Lerer, Adam and Bradbury, James and Chanan, Gregory and Killeen, Trevor and Lin, Zeming and Gimelshein, Natalia and Antiga, Luca and Desmaison, Alban and Kopf, Andreas and Yang, Edward and DeVito, Zachary and Raison, Martin and Tejani, Alykhan and Chilamkurthy, Sasank and Steiner, Benoit and Fang, Lu and Bai, Junjie and Chintala, Soumith},
    booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
    volume={32},
    pages = {8024--8035},
    year = {2019},
    url = {http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf},
    address="Vancouver, Canada"
}

@article{hill2011ihdp,
  title={Bayesian nonparametric modeling for causal inference},
  author={Hill, Jennifer L},
  journal={Journal of Computational and Graphical Statistics},
  volume={20},
  pages={217--240},
  year={2011},
  publisher={Taylor \& Francis}
}

@article{lalonde1986jobs,
  title={Evaluating the econometric evaluations of training programs with experimental data},
  author={LaLonde, Robert J},
  journal={The American Economic Review},
  pages={604--620},
  year={1986},
  publisher={JSTOR}
}

@article{fanaee2014bikerental,
  title={Event labeling combining ensemble detectors and background knowledge},
  author={Fanaee-T, Hadi and Gama, Joao},
  journal={Progress in Artificial Intelligence},
  volume={2},
  pages={113--127},
  year={2014},
  publisher={Springer}
}

% Phoenix experiment

@misc{phoenix2022dataset, 
  title={Employee compensation}, 
  howpublished = {City of Phoenix Open Data. \url{https://www.phoenixopendata.com/dataset/employee-compensation}}, 
  note={Accessed on May 2022}
}

@misc{genderize,
  title={Genderize},
  howpublished={Demografix ApS. \url{https://genderize.io/}},
  note={Accessed on May 2022}
}


% New references based on reviewer feedback

@inproceedings{wang2021shapley,
  title={Shapley flow: A graph-based approach to interpreting model predictions},
  author={Wang, Jiaxuan and Wiens, Jenna and Lundberg, Scott},
  booktitle={International Conference on Artificial Intelligence and Statistics},
  pages={721--729},
  year={2021},
  organization={PMLR}
}

@inproceedings{luther2023sage,
  title={Efficient SAGE Estimation via Causal Structure Learning},
  author={Luther, Christoph and K\"onig, Gunnar and Grosse-Wentrup, Moritz},
  booktitle={Proceedings of The 26th International Conference on Artificial Intelligence and Statistics (AISTATS)},
  pages={11650--11670},
  year={2023},
  publisher={PMLR}
}

@article{zhang2025quantifying,
  title={Quantifying variable contributions to bus operation delays considering causal relationships},
  author={Zhang, Qi and Ma, Zhenliang and Wu, Yuanyuan and Liu, Yang and Qu, Xiaobo},
  journal={Transportation Research Part E: Logistics and Transportation Review},
  volume={194},
  pages={103881},
  year={2025},
  publisher={Elsevier}
}

@inproceedings{
    parafita2025practical,
    title={Practical do-Shapley Explanations with Estimand-Agnostic Causal Inference},
    author={Parafita, {\'A}lvaro and Garriga, Tomas and Brando, Axel and Cazorla, Francisco J},
    booktitle={The Thirty-ninth Annual Conference on Neural Information Processing Systems},
    year={2025},
    url={https://openreview.net/forum?id=Qabko39AS5}
}

% added by max - might be inconsitent to rest of the style (even though I tried)
@article{lundberg2020fromlocal,
 title={{From local explanations to global understanding with explainable AI for trees}},
  author={Scott M. Lundberg and Gabriel G. Erion and Hugh Chen and Alex J. DeGrave and Jordan M. Prutkin and Bala Nair and Ronit Katz and Jonathan Himmelfarb and Nisha Bansal and Su{-}In Lee},
  year=2020,
  journal={Nature Machine Intelligence},
  volume=2,
  number=1,
  pages={56--67}
}
@inproceedings{yu2022linear,
  title={Linear tree shap},
  author={Peng Yu and Albert Bifet and Jesse Read and Chao Xu},
  booktitle={Advances in Neural Information Processing Systems (NeurIPS)},
  volume={35},
  year={2022}
}

@inproceedings{zern2023interventional,
  title={Interventional {SHAP} Values and Interaction Values for Piecewise Linear Regression Trees},
  author={Artjom Zern and Klaus Broelemann and Gjergji Kasneci},
  year={2023},
  booktitle={Proceedings of the 37th AAAI Conference on Artificial Intelligence},
  volume={37},
  pages={11164--11173}
}
@inproceedings{muschalik2024beyond,
  title={{Beyond TreeSHAP: Efficient Computation of Any-Order Shapley Interactions for Tree Ensembles}},
  author={Maximilian Muschalik and Fabian Fumagalli and Barbara Hammer and Eyke H{\"{u}}llermeier},
  year=2024,
  booktitle={Proceedings of the 38th AAAI Conference on Artificial Intelligence},
  volume={38},
  pages={14388--14396}
}
@inproceedings{muschalik2025exact,
  title={{Exact Computation of Any-Order Shapley Interactions for Graph Neural Networks}},
  author={Maximilian Muschalik and Fabian Fumagalli and Paolo Frazzetto and Janine Strotherm and Luca Hermes and Alessandro Sperduti and Eyke H{\"u}llermeier and Barbara Hammer},
  booktitle={The Thirteenth International Conference on Learning Representations},
  year={2025}
}
@article{mohammadi2025computing,
  title={Computing Exact Shapley Values in Polynomial Time for Product-Kernel Methods},
  author={Mohammadi, Majid and Chau, Siu Lun and Muandet, Krikamol},
  journal={arXiv preprint arXiv:2505.16516},
  year={2025}
}
@article{mohammadi2025exact,
  title={Exact Shapley Attributions in Quadratic-time for FANOVA Gaussian Processes}, 
  author={Majid Mohammadi and Krikamol Muandet and Ilaria Tiddi and Annette Ten Teije and Siu Lun Chau},
  year={2025},
  journal={arXiv preprint arXiv:2508.14499}
}
@inproceedings{jia2019towards,
  author={Ruoxi Jia and David Dao and Boxin Wang and Frances Ann Hubis and Nick Hynes and Nezihe Merve G{\"{u}}rel and Bo Li and Ce Zhang and Dawn Song and Costas J. Spanos},
  title={Towards Efficient Data Valuation Based on the Shapley Value},
  booktitle={Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics (AIStats)},
  pages        = {1167--1176},
  publisher    = {PMLR},
  year         = {2019}
}
@inproceedings{wang2023privacy,
  title={A Privacy-Friendly Approach to Data Valuation},
  author={Jiachen T. Wang and Yuqing Zhu and Yu-Xiang Wang and Ruoxi Jia and Prateek Mittal},
  booktitle={Advances in Neural Information Processing Systems (NeurIPS)},
  volume={37},
  year={2023},
}
@inproceedings{wang2024efficient,
  author={Jiachen T. Wang and Prateek Mittal and Ruoxi Jia},
  title={Efficient Data Shapley for Weighted Nearest Neighbor Algorithms},
  booktitle={Proceedings of the 27th International Conference on Artificial Intelligence and Statistics (AIStats)},  
  pages={2557--2565},
  publisher={PMLR},
  year={2024},
}
@inproceedings{muschalik2024shapiq,
  title={{shapiq: Shapley Interactions for Machine Learning}},
  author= {Maximilian Muschalik and Hubert Baniecki and Fabian Fumagalli and Patrick Kolpaczki and Barbara Hammer and Eyke H{\"{u}}llermeier},
  year=2024,
  booktitle={Advances in Neural Information Processing Systems (NeurIPS)},
  volume=37,
  pages={130324--130357}
}

@inproceedings{langley00,
 author    = {P. Langley},
 title     = {Crafting Papers on Machine Learning},
 year      = {2000},
 pages     = {1207--1216},
 editor    = {Pat Langley},
 booktitle     = {Proceedings of the 17th International Conference
              on Machine Learning (ICML 2000)},
 address   = {Stanford, CA},
 publisher = {Morgan Kaufmann}
}

@TechReport{mitchell80,
  author = 	 "T. M. Mitchell",
  title = 	 "The Need for Biases in Learning Generalizations",
  institution =  "Computer Science Department, Rutgers University",
  year = 	 "1980",
  address =	 "New Brunswick, MA",
}

@phdthesis{kearns89,
  author = {M. J. Kearns},
  title =  {Computational Complexity of Machine Learning},
  school = {Department of Computer Science, Harvard University},
  year =   {1989}
}

@Book{MachineLearningI,
  editor = 	 "R. S. Michalski and J. G. Carbonell and T.
		  M. Mitchell",
  title = 	 "Machine Learning: An Artificial Intelligence
		  Approach, Vol. I",
  publisher = 	 "Tioga",
  year = 	 "1983",
  address =	 "Palo Alto, CA"
}

@Book{DudaHart2nd,
  author =       "R. O. Duda and P. E. Hart and D. G. Stork",
  title =        "Pattern Classification",
  publisher =    "John Wiley and Sons",
  edition =      "2nd",
  year =         "2000"
}

@misc{anonymous,
  title= {Suppressed for Anonymity},
  author= {Author, N. N.},
  year= {2021}
}

@InCollection{Newell81,
  author =       "A. Newell and P. S. Rosenbloom",
  title =        "Mechanisms of Skill Acquisition and the Law of
                  Practice", 
  booktitle =    "Cognitive Skills and Their Acquisition",
  pages =        "1--51",
  publisher =    "Lawrence Erlbaum Associates, Inc.",
  year =         "1981",
  editor =       "J. R. Anderson",
  chapter =      "1",
  address =      "Hillsdale, NJ"
}


@Article{Samuel59,
  author = 	 "A. L. Samuel",
  title = 	 "Some Studies in Machine Learning Using the Game of
		  Checkers",
  journal =	 "IBM Journal of Research and Development",
  year =	 "1959",
  volume =	 "3",
  number =	 "3",
  pages =	 "211--229"
}

@book{odonnell2014analysis,
  title={Analysis of Boolean functions},
  author={O'Donnell, Ryan},
  year={2014},
  publisher={Cambridge University Press}
}

@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{mitchell2022sampling,
  title={Sampling permutations for shapley value estimation},
  author={Mitchell, Rory and Cooper, Joshua and Frank, Eibe and Holmes, Geoffrey},
  journal={Journal of Machine Learning Research},
  volume={23},
  number={43},
  pages={1--46},
  year={2022}
}

@InProceedings{10.1007/978-3-032-08324-1_9,
author="Olsen, Lars Henry Berge
and Jullum, Martin",
editor="Guidotti, Riccardo
and Schmid, Ute
and Longo, Luca",
title="Improving the Weighting Strategy in KernelSHAP",
booktitle="Explainable Artificial Intelligence",
year="2026",
publisher="Springer Nature Switzerland",
address="Cham",
pages="194--218",
isbn="978-3-032-08324-1"
}

@book{grabisch2016set,
  title={Set functions, games and capacities in decision making},
  author={Grabisch, Michel and others},
  volume={46},
  year={2016},
  publisher={Springer}
}

@inproceedings{
kang.2025,
title={{SPEX}: Scaling Feature Interaction Explanations for {LLM}s},
author={Justin Singh Kang and Landon Butler and Abhineet Agarwal and Yigit Efe Erginbas and Ramtin Pedarsani and Bin Yu and Kannan Ramchandran},
booktitle={Forty-second International Conference on Machine Learning},
year={2025},
url={https://openreview.net/forum?id=pRlKbAwczl}
}

@inproceedings{
butler.2025,
title={Proxy-{SPEX}: Sample-Efficient Interpretability via Sparse Feature Interactions in {LLM}s},
author={Landon Butler and Abhineet Agarwal and Justin Singh Kang and Yigit Efe Erginbas and Bin Yu and Kannan Ramchandran},
booktitle={The Thirty-ninth Annual Conference on Neural Information Processing Systems},
year={2025},
url={https://openreview.net/forum?id=KI8qan2EA7}
}



@inproceedings{
gorji2025shap,
title={{SHAP} values via sparse Fourier representation},
author={Ali Gorji and Andisheh Amrollahi and Andreas Krause},
booktitle={The Thirty-ninth Annual Conference on Neural Information Processing Systems},
year={2025},
url={https://openreview.net/forum?id=wab4BEAUt6}
}

@article{kang2024learning,
  title={Learning to understand: Identifying interactions via the M{\"o}bius transform},
  author={Kang, Justin and Erginbas, Yigit Efe and Butler, Landon and Pedarsani, Ramtin and Ramchandran, Kannan},
  journal={Advances in Neural Information Processing Systems},
  volume={37},
  pages={46160--46202},
  year={2024}
}

@conference{008490a367af4c898494461b6554a770,
title = "Deep learning generalizes because the parameter-function map is biased towards simple functions",
abstract = "Deep neural networks (DNNs) generalize remarkably well without explicit regularization even in the strongly over-parametrized regime where classical learning theory would instead predict that they would severely overfit. While many proposals for some kind of implicit regularization have been made to rationalise this success, there is no consensus for the fundamental reason why DNNs do not strongly overfit. In this paper, we provide a new explanation. By applying a very general probability-complexity bound recently derived from algorithmic information theory (AIT), we argue that the parameter-function map of many DNNs should be exponentially biased towards simple functions. We then provide clear evidence for this strong bias in a model DNN for Boolean functions, as well as in much larger fully conected and convolutional networks trained on CIFAR10 and MNIST. As the target functions in many real problems are expected to be highly structured, this intrinsic simplicity bias helps explain why deep networks generalize well on real world problems. This picture also facilitates a novel PAC-Bayes approach where the prior is taken over the DNN input-output function space, rather than the more conventional prior over parameter space. If we assume that the training algorithm samples parameters close to uniformly within the zero-error region then the PAC-Bayes theorem can be used to guarantee good expected generalization for target functions producing high-likelihood training sets. By exploiting recently discovered connections between DNNs and Gaussian processes to estimate the marginal likelihood, we produce relatively tight generalization PAC-Bayes error bounds which correlate well with the true error on realistic datasets such as MNIST and CIFAR10and for architectures including convolutional and fully connected networks.",
author = "P{\'e}rez, \{Guillermo Valle\} and Louis, \{Ard A.\} and Camargo, \{Chico Q.\}",
note = "Publisher Copyright: {\textcopyright} 7th International Conference on Learning Representations, ICLR 2019. All Rights Reserved.; 7th International Conference on Learning Representations, ICLR 2019 ; Conference date: 06-05-2019 Through 09-05-2019",
year = "2019",
language = "English",
}

@inproceedings{
ren2024towards,
title={Towards the Dynamics of a {DNN} Learning Symbolic Interactions},
author={Qihan Ren and Junpeng Zhang and Yang Xu and Yue Xin and Dongrui Liu and Quanshi Zhang},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=dIHXwKjXRE}
}

@inproceedings{
witter2025regressionadjusted,
title={Regression-adjusted Monte Carlo Estimators for Shapley Values and Probabilistic Values},
author={R. Teal Witter and Yurong Liu and Christopher Musco},
booktitle={The Thirty-ninth Annual Conference on Neural Information Processing Systems},
year={2025},
url={https://openreview.net/forum?id=Qabko39AS5}
}

@article{lundberg2020local,
  title={From local explanations to global understanding with explainable AI for trees},
  author={Lundberg, Scott M and Erion, Gabriel and Chen, Hugh and DeGrave, Alex and Prutkin, Jordan M and Nair, Bala and Katz, Ronit and Himmelfarb, Jonathan and Bansal, Nisha and Lee, Su-In},
  journal={Nature machine intelligence},
  volume={2},
  number={1},
  pages={56--67},
  year={2020},
  publisher={Nature Publishing Group}
}

@incollection{charnes1988extremal,
  title={Extremal principle solutions of games in characteristic function form: core, Chebychev and Shapley value generalizations},
  author={Charnes, Abraham and Golany, Boaz and Keane, M and Rousseau, J},
  booktitle={Econometrics of planning and efficiency},
  pages={123--133},
  year={1988},
  publisher={Springer}
}

@article{Aas.2021,
	title        = {Explaining individual predictions when features are dependent: More accurate approximations to {Shapley} values},
	author       = {Kjersti Aas and Martin Jullum and Anders Løland},
	year         = 2021,
	journal      = {Artificial Intelligence},
	volume       = 298,
	pages        = 103502
}
@article{Achanta.2012,
	title        = {SLIC Superpixels Compared to State-of-the-Art Superpixel Methods},
	author       = {Radhakrishna Achanta and Appu Shaji and Kevin Smith and Aur{\'{e}}lien Lucchi and Pascal Fua and Sabine S{\"{u}}sstrunk},
	year         = 2012,
	journal      = {{IEEE} Transactions on Pattern Analysis and Machine Intelligence},
	volume       = 34,
	number       = 11,
	pages        = {2274--2282},
	doi          = {10.1109/TPAMI.2012.120}
}
@article{Adadi.2022,
	title        = {{Peeking Inside the Black-Box: A Survey on Explainable Artificial Intelligence (XAI)}},
	author       = {Amina Adadi and Mohammed Berrada},
	year         = 2018,
	journal      = {{IEEE} Access},
	volume       = 6,
	pages        = {52138--52160},
	doi          = {10.1109/ACCESS.2018.2870052}
}
@inproceedings{Agarwal.2022,
	title        = {{OpenXAI}: Towards a Transparent Evaluation of Model Explanations},
	author       = {Chirag Agarwal and Satyapriya Krishna and Eshika Saxena and Martin Pawelczyk and Nari Johnson and Isha Puri and Marinka Zitnik and Himabindu Lakkaraju},
	year         = 2022,
	booktitle    = {Proceedings of Advances in Neural Information Processing Systems {(NeurIPS)}}
}
@article{Ahmed.2022,
	title        = {From Artificial Intelligence to Explainable Artificial Intelligence in Industry 4.0: {A} Survey on What, How, and Where},
	author       = {Imran Ahmed and Gwanggil Jeon and Francesco Piccialli},
	year         = 2022,
	journal      = {{IEEE} Transactions on Industrial Informatics},
	volume       = 18,
	number       = 8,
	pages        = {5031--5042},
	doi          = {10.1109/TII.2022.3146552}
}
@article{Alber.2019,
	title        = {{iNNvestigate} Neural Networks!},
	author       = {Maximilian Alber and Sebastian Lapuschkin and Philipp Seegerer and Miriam H{{\"a}}gele and Kristof T. Sch{{\"u}}tt and Gr{{\'e}}goire Montavon and Wojciech Samek and Klaus-Robert M{{\"u}}ller and Sven D{{\"a}}hne and Pieter-Jan Kindermans},
	year         = 2019,
	journal      = {Journal of Machine Learning Research},
	volume       = 20,
	number       = 93,
	pages        = {1--8},
	doi          = {10.48550/arXiv.1808.04260}
}
@misc{Angwin.2016,
	title        = {{Machine Bias. There's software used across the country to predict future criminals. And it's biased against blacks.}},
	author       = {Angwin, J. and Larson, J. and Mattu, S. and Kirchner, L.},
	year         = 2016,
	publisher    = {ProPublica}
}
@article{Arya.2020,
	title        = {{AI Explainability 360}: An Extensible Toolkit for Understanding Data and Machine Learning Models},
	author       = {Vijay Arya and Rachel K. E. Bellamy and Pin-Yu Chen and Amit Dhurandhar and Michael Hind and Samuel C. Hoffman and Stephanie Houde and Q. Vera Liao and Ronny Luss and Aleksandra Mojsilović and Sami Mourad and Pablo Pedemonte and Ramya Raghavendra and John T. Richards and Prasanna Sattigeri and Karthikeyan Shanmugam and Moninder Singh and Kush R. Varshney and Dennis Wei and Yunfeng Zhang},
	year         = 2020,
	journal      = {Journal of Machine Learning Research},
	volume       = 21,
	number       = 130,
	pages        = {1--6}
}
@inproceedings{Balestra.2022,
	title        = {Unsupervised Features Ranking via Coalitional Game Theory for Categorical Data},
	author       = {Chiara Balestra and Florian Huber and Andreas Mayr and Emmanuel M{\"{u}}ller},
	year         = 2022,
	booktitle    = {Proceedings of Big Data Analytics and Knowledge Discovery {(DaWaK)}},
	pages        = {97--111}
}
@article{Baniecki.2021,
	title        = {dalex: Responsible Machine Learning with Interactive Explainability and Fairness in {Python}},
	author       = {Hubert Baniecki and Wojciech Kretowicz and Piotr Piatyszek and Jakub Wisniewski and Przemyslaw Biecek},
	year         = 2021,
	journal      = {Journal of Machine Learning Research},
	volume       = 22,
	number       = 214,
	pages        = {1--7},
	doi          = {10.48550/arXiv.2012.14406}
}
@article{Baniecki.2024,
	title        = {The grammar of interactive explanatory model analysis},
	author       = {Baniecki, Hubert and Parzych, Dariusz and Biecek, Przemyslaw},
	year         = 2024,
	journal      = {Data Mining and Knowledge Discovery},
	volume       = 38,
	pages        = {2596--2632}
}
@article{Banzhaf.1964,
	title        = {Weighted voting doesn't work: A mathematical analysis},
	author       = {Banzhaf III, John F},
	year         = 1964,
	journal      = {Rutgers Law Review},
	publisher    = {HeinOnline},
	volume       = 19,
	pages        = 317
}
@article{Başağaoğlu.2022,
	title        = {A Review on Interpretable and Explainable Artificial Intelligence in Hydroclimatic Applications},
	author       = {Başağaoğlu, Hakan and Chakraborty, Debaditya and Lago, Cesar Do and Gutierrez, Lilianna and Şahinli, Mehmet Arif and Giacomoni, Marcio and Furl, Chad and Mirchi, Ali and Moriasi, Daniel and Şengör, Sema Sevinç},
	year         = 2022,
	journal      = {Water},
	volume       = 14,
	number       = 8,
	doi          = {10.3390/w14081230}
}
@inproceedings{Bischl.2021,
	title        = {Open{ML} Benchmarking Suites},
	author       = {Bernd Bischl and Giuseppe Casalicchio and Matthias Feurer and Pieter Gijsbers and Frank Hutter and Michel Lang and Rafael Gomes Mantovani and Jan N. van Rijn and Joaquin Vanschoren},
	year         = 2021,
	booktitle    = {Proceedings of Advances in Neural Information Processing Systems {(NeurIPS)}}
}
@inproceedings{Bord.2023,
	title        = {{From Shapley Values to Generalized Additive Models and back}},
	author       = {Sebastian Bordt and Ulrike von Luxburg},
	year         = 2023,
	booktitle    = {Proceedings of the International Conference on Artificial Intelligence and Statistics {(AISTATS)}},
	pages        = {709--745}
}
@article{Boulesteix.2012,
	title        = {{Random forest Gini importance favours SNPs with large minor allele frequency: impact, sources and recommendations}},
	author       = {Boulesteix, Anne-Laure and Bender, Andreas and Lorenzo Bermejo, Justo and Strobl, Carolin},
	year         = 2012,
	journal      = {Briefings in Bioinformatics},
	volume       = 13,
	number       = 3,
	pages        = {292--304},
	doi          = {10.1093/bib/bbr053}
}
@article{Breiman.2001,
	title        = {Random Forests},
	author       = {Leo Breiman},
	year         = 2001,
	journal      = {Machine Learning},
	volume       = 45,
	number       = 1,
	pages        = {5--32},
	doi          = {10.1023/A:1010933404324}
}
@inproceedings{Casalicchio.2018,
	title        = {Visualizing the Feature Importance for Black Box Models},
	author       = {Giuseppe Casalicchio and Christoph Molnar and Bernd Bischl},
	year         = 2018,
	booktitle    = {Machine Learning and Knowledge Discovery in Databases - European Conference ({ECML} {PKDD})},
	publisher    = {Springer},
	series       = {Lecture Notes in Computer Science},
	volume       = 11051,
	pages        = {655--670},
	doi          = {10.1007/978-3-030-10925-7\_40}
}
@article{Castro.2009,
	title        = {{Polynomial calculation of the Shapley value based on sampling}},
	author       = {Javier Castro and Daniel G{\'{o}}mez and Juan Tejada},
	year         = 2009,
	journal      = {Computers \& Operations Research},
	volume       = 36,
	number       = 5,
	pages        = {1726--1730},
	doi          = {10.1016/j.cor.2008.04.004}
}
@article{Castro.2017,
	title        = {{Improving polynomial estimation of the Shapley value by stratified random sampling with optimum allocation}},
	author       = {Javier Castro and Daniel G{\'{o}}mez and Elisenda Molina and Juan Tejada},
	year         = 2017,
	journal      = {Computers \& Operations Research},
	volume       = 82,
	pages        = {180--188},
	doi          = {10.1016/j.cor.2017.01.019}
}
@inbook{Charnes.1988,
	title        = {Extremal Principle Solutions of Games in Characteristic Function Form: Core, Chebychev and Shapley Value Generalizations},
	author       = {Charnes, A. and Golany, B. and Keane, M. and Rousseau, J.},
	year         = 1988,
	booktitle    = {Econometrics of Planning and Efficiency},
	publisher    = {Springer Netherlands},
	volume       = 11,
	pages        = {123–133},
	doi          = {10.1007/978-94-009-3677-5_7},
	collection   = {Advanced Studies in Theoretical and Applied Econometrics}
}
@inproceedings{Chen.2016,
	title        = {{XGBoost}: A Scalable Tree Boosting System},
	author       = {Chen, Tianqi and Guestrin, Carlos},
	year         = 2016,
	booktitle    = {Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD)},
	publisher    = {ACM},
	pages        = {785--794},
	doi          = {10.1145/2939672.2939785},
	numpages     = 10
}
@article{Chen.2020,
	title        = {{True to the Model or True to the Data?}},
	author       = {Hugh Chen and Joseph D. Janizek and Scott M. Lundberg and Su{-}In Lee},
	year         = 2020,
	journal      = {CoRR},
	volume       = {2006.16234}
}
@article{Chen.2023,
	title        = {{Algorithms to estimate Shapley value feature attributions}},
	author       = {Chen, H. and Covert, I.C. and Lundberg, S.M. and others},
	year         = 2023,
	journal      = {Nature Machine Intelligence},
	volume       = 5,
	pages        = {590–601},
	doi          = {10.1038/s42256-023-00657-x}
}
@article{Cohen.2007,
	title        = {Feature Selection via Coalitional Game Theory},
	author       = {Shay B. Cohen and Gideon Dror and Eytan Ruppin},
	year         = 2007,
	journal      = {Neural Computing},
	volume       = 19,
	number       = 7,
	pages        = {1939--1961}
}
@inproceedings{Covert.2020,
	title        = {{Understanding Global Feature Contributions With Additive Importance Measures}},
	author       = {Ian Covert and Scott M. Lundberg and Su{-}In Lee},
	year         = 2020,
	booktitle    = {Proceedings of Advances in Neural Information Processing Systems {(NeurIPS)}}
}
@inproceedings{Covert.2021,
	title        = {{Improving KernelSHAP: Practical Shapley Value Estimation Using Linear Regression}},
	author       = {Ian Covert and Su{-}In Lee},
	year         = 2021,
	booktitle    = {Proceedings of the International Conference on Artificial Intelligence and Statistics {(AISTATS)}},
	pages        = {3457--3465}
}
@article{Covert.2021b,
	title        = {{Explaining by Removing: A Unified Framework for Model Explanation}},
	author       = {Ian Covert and Scott M. Lundberg and Su{-}In Lee},
	year         = 2021,
	journal      = {Journal of Machine Learning Research},
	volume       = 22,
	number       = 209,
	pages        = {1--90},
	doi          = {10.5555/3546258.3546467}
}
@inproceedings{Covert.2023,
	title        = {Learning to Estimate Shapley Values with Vision Transformers},
	author       = {Ian Connick Covert and Chanwoo Kim and Su{-}In Lee},
	year         = 2023,
	booktitle    = {Proceedings of the International Conference on Learning Representations ({ICLR})}
}
@inproceedings{Cui.2020,
	title        = {Learning Global Pairwise Interactions with Bayesian Neural Networks},
	author       = {Tianyu Cui and Pekka Marttinen and Samuel Kaski},
	year         = 2020,
	booktitle    = {24th European Conference on Artificial Intelligence, {(PAIS 2020)}},
	publisher    = {{IOS} Press},
	series       = {Frontiers in Artificial Intelligence and Applications},
	volume       = 325,
	pages        = {1087--1094},
	doi          = {10.3233/FAIA200205}
}
@article{Dawson.1995,
	title        = {{The “Unusual Episode” Data Revisited}},
	author       = {Dawson, R. J. M.},
	year         = 1995,
	journal      = {Journal of Statistics Education},
	volume       = 3,
	number       = 3
}
@inproceedings{Rozemberczki.2022,
	title        = {The Shapley Value in Machine Learning},
	author       = {Benedek Rozemberczki and Lauren Watson and P{\'{e}}ter Bayer and Hao{-}Tsung Yang and Oliver Kiss and Sebastian Nilsson and Rik Sarkar},
	year         = 2022,
	booktitle    = {Proceedings of International Joint Conference on Artificial Intelligence {(IJCAI)}},
	pages        = {5572--5579}
}
@article{Marichal.2007,
	title        = {Axiomatic characterizations of generalized values},
	author       = {Jean{-}Luc Marichal and Ivan Kojadinovic and Katsushige Fujimoto},
	year         = 2007,
	journal      = {Discrete Applied Mathematics},
	volume       = 155,
	number       = 1,
	pages        = {26--43},
	doi          = {10.1016/J.DAM.2006.05.002}
}
@article{Albahri.2023,
	title        = {A systematic review of trustworthy and explainable artificial intelligence in healthcare: Assessment of quality, bias risk, and data fusion},
	author       = {Ahmed Shihab Albahri and Ali Mohammed and Mohammed A. Fadhel and Alhamzah Alnoor and Noor S. Baqer and Laith Alzubaidi and Osamah Shihab Albahri and Abdullah Hussein Alamoodi and Jinshuai Bai and Asma Salhi and Jos{\'{e}} Santamar{\'{\i}}a and Chun Ouyang and Ashish Gupta and Yuantong Gu and Muhammet Deveci},
	year         = 2023,
	journal      = {Information Fusion},
	volume       = 96,
	pages        = {156--191},
	doi          = {10.1016/J.INFFUS.2023.03.008}
}
@article{Wang.2022,
	title        = {Effects of Explanations in AI-Assisted Decision Making: Principles and Comparisons},
	author       = {Xinru Wang and Ming Yin},
	year         = 2022,
	journal      = {{ACM} Transactions on Interactive Intelligent Systems},
	volume       = 12,
	number       = 4,
	pages        = {27:1--27:36},
	doi          = {10.1145/3519266}
}
@article{Deng.1994,
	title        = {On the Complexity of Cooperative Solution Concepts},
	author       = {Xiaotie Deng and Christos H. Papadimitriou},
	year         = 1994,
	journal      = {Mathematics of Operations Research},
	volume       = 19,
	number       = 2,
	pages        = {257--266},
	doi          = {10.1287/moor.19.2.257}
}
@inproceedings{Deng.2009,
	title        = {ImageNet: A large-scale hierarchical image database},
	author       = {Jia Deng and Wei Dong and Richard Socher and Li{-}Jia Li and Kai Li and Li Fei{-}Fei},
	year         = 2009,
	booktitle    = {Proceedings of {IEEE} Computer Society Conference on Computer Vision and Pattern Recognition {(CVPR)}},
	pages        = {248--255}
}
@article{Deng.2024,
	title        = {{Unifying Fourteen Post-hoc Attribution Methods with Taylor Interactions}},
	author       = {Deng, Huiqi and Zou, Na and Du, Mengnan and Chen, Weifu and Feng, Guocan and Yang, Ziwei and Li, Zheyang and Zhang, Quanshi},
	year         = 2024,
	journal      = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
	pages        = {1--17},
	doi          = {10.1109/TPAMI.2024.3358410}
}
@inproceedings{Deng.2022,
	title        = {Discovering and Explaining the Representation Bottleneck of {DNNS}},
	author       = {Huiqi Deng and Qihan Ren and Hao Zhang and Quanshi Zhang},
	year         = 2022,
	booktitle    = {Proceedings of the International Conference on Learning Representations ({ICLR})}
}
@article{Denneberg.1999,
	title        = {Interaction Transform of Set Functions over a Finite Set},
	author       = {Denneberg, Dieter and Grabisch, Michel},
	year         = 1999,
	journal      = {Information Sciences},
	volume       = 121,
	number       = {1–2},
	pages        = {149–170},
	doi          = {10.1016/S0020-0255(99)00099-7}
}
@article{Ding.2008,
	title        = {Formulas for approximating pseudo-Boolean random variables},
	author       = {Guoli Ding and Robert F. Lax and Jianhua Chen and Peter P. Chen},
	year         = 2008,
	journal      = {Discrete Applied Mathematics},
	volume       = 156,
	number       = 10,
	pages        = {1581--1597},
	doi          = {10.1016/j.dam.2007.08.034}
}
@article{Dubey.1981,
	title        = {Value Theory Without Efficiency},
	author       = {Pradeep Dubey and Abraham Neyman and Robert James Weber},
	year         = 1981,
	journal      = {Mathematics of Operations Research},
	volume       = 6,
	number       = 1,
	pages        = {122--128},
	doi          = {10.1287/MOOR.6.1.122}
}
@article{FanaeeT.2014,
	title        = {{Event Labeling Combining Ensemble Detectors and Background Knowledge}},
	author       = {Fanaee-T, H. and Gama, J.},
	year         = 2014,
	journal      = {Progress in Artificial Intelligence},
	volume       = 2,
	number       = 2,
	pages        = {113--127},
	doi          = {10.1007/s13748-013-0040-3}
}
@article{Feurer.2020,
	title        = {{OpenML-Python: an extensible Python API for OpenML}},
	author       = {Matthias Feurer and Jan N. van Rijn and Arlind Kadra and Pieter Gijsbers and Neeratyoy Mallik and Sahithya Ravi and Andreas Mueller and Joaquin Vanschoren and Frank Hutter},
	year         = 2020,
	journal      = {CoRR},
	volume       = {abs/1911.02490}
}
@article{Fisher.2019,
	title        = {All Models are Wrong, but Many are Useful: Learning a Variable's Importance by Studying an Entire Class of Prediction Models Simultaneously},
	author       = {Aaron Fisher and Cynthia Rudin and Francesca Dominici},
	year         = 2019,
	journal      = {Journal of Machine Learning Research},
	volume       = 20,
	number       = 177,
	pages        = {1--81}
}
@article{Friedman.2001,
	title        = {{Greedy function approximation: A gradient boosting machine.}},
	author       = {Jerome H. Friedman},
	year         = 2001,
	journal      = {The Annals of Statistics},
	publisher    = {Institute of Mathematical Statistics},
	volume       = 29,
	number       = 5,
	pages        = {1189--1232},
	doi          = {10.1214/aos/1013203451}
}
@inproceedings{Frye.2021,
	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},
	year         = 2021,
	booktitle    = {Proceedings of the International Conference on Learning Representations ({ICLR})}
}
@article{Fujimoto.2006,
	title        = {{Axiomatic characterizations of probabilistic and cardinal-probabilistic interaction indices}},
	author       = {Katsushige Fujimoto and Ivan Kojadinovic and Jean{-}Luc Marichal},
	year         = 2006,
	journal      = {Games and Economic Behavior},
	volume       = 55,
	number       = 1,
	pages        = {72--99},
	doi          = {10.1016/j.geb.2005.03.002}
}
@inproceedings{Fumagalli.2023,
	title        = {{SHAP-IQ: Unified Approximation of any-order Shapley Interactions}},
	author       = {Fabian Fumagalli and Maximilian Muschalik and Patrick Kolpaczki and Eyke H{\"u}llermeier and Barbara Hammer},
	year         = 2023,
	booktitle    = {Proceedings of Advances in Neural Information Processing Systems {(NeurIPS)}},
keywords = {FF},
}
@inproceedings{Fumagalli.2024,
	title        = {{KernelSHAP-IQ: Weighted Least Square Optimization for Shapley Interactions}},
	author       = {Fabian Fumagalli and Maximilian Muschalik and Patrick Kolpaczki and Eyke H{\"{u}}llermeier and Barbara Hammer},
	year         = 2024,
	booktitle    = {Proceedings of the International Conference on Machine Learning ({ICML})},
keywords = {FF},
}
@inproceedings{Ghorbani.2019,
	title        = {Data Shapley: Equitable Valuation of Data for Machine Learning},
	author       = {Amirata Ghorbani and James Y. Zou},
	year         = 2019,
	booktitle    = {Proceedings of the International Conference on Machine Learning {(ICML)}},
	pages        = {2242--2251}
}
@article{Grabisch.1997,
	title        = {k-order additive discrete fuzzy measures and their representation},
	author       = {Grabisch, Michel},
	year         = 1997,
	journal      = {Fuzzy Sets and Systems},
	volume       = 92,
	number       = 2,
	pages        = {167–189},
	doi          = {10.1016/S0165-0114(97)00168-1}
}
@article{Grabisch.1999,
	title        = {{An axiomatic approach to the concept of interaction among players in cooperative games}},
	author       = {Michel Grabisch and Marc Roubens},
	year         = 1999,
	journal      = {International Journal of Game Theory},
	volume       = 28,
	number       = 4,
	pages        = {547--565},
	doi          = {10.1007/s001820050125}
}
@article{Grabisch.2000,
	title        = {Equivalent Representations of Set Functions},
	author       = {Michel Grabisch and Jean{-}Luc Marichal and Marc Roubens},
	year         = 2000,
	journal      = {Mathematics of Operations Research},
	volume       = 25,
	number       = 2,
	pages        = {157--178},
	doi          = {10.1287/moor.25.2.157.12225}
}
@book{Grabisch.2016,
	title        = {Set Functions, Games and Capacities in Decision Making},
	author       = {Grabisch, Michel},
	year         = 2016,
	publisher    = {Springer International Publishing Switzerland},
	volume       = 46,
	doi          = {10.1007/978-3-319-30690-2},
	isbn         = {978-3-319-30690-2}
}
@article{Hammer.1992,
	title        = {Approximations of pseudo-Boolean functions; applications to game theory},
	author       = {Peter L. Hammer and Ron Holzman},
	year         = 1992,
	journal      = {{ZOR} Mathematical Methods of Operations Research},
	volume       = 36,
	number       = 1,
	pages        = {3--21},
	doi          = {10.1007/BF01541028}
}
@inproceedings{Harris.2022,
	title        = {Joint Shapley values: a measure of joint feature importance},
	author       = {Chris Harris and Richard Pymar and Colin Rowat},
	year         = 2022,
	booktitle    = {Proceedings of the International Conference on Learning Representations ({ICLR})}
}
@article{Harsanyi.1963,
	title        = {A simplified bargaining model for the n-person cooperative game},
	author       = {Harsanyi, John C},
	year         = 1963,
	journal      = {International Economic Review},
	publisher    = {JSTOR},
	volume       = 4,
	number       = 2,
	pages        = {194--220}
}
@inproceedings{He.2018,
	title        = {Deep Residual Learning for Image Recognition},
	author       = {Kaiming He and Xiangyu Zhang and Shaoqing Ren and Jian Sun},
	year         = 2016,
	booktitle    = {Proceedings of {IEEE} Computer Society Conference on Computer Vision and Pattern Recognition {(CVPR)}},
	pages        = {770--778}
}
@article{Hedstrom.2023,
	title        = {Quantus: An Explainable {AI} Toolkit for Responsible Evaluation of Neural Network Explanations and Beyond},
	author       = {Anna Hedstrom and Leander Weber and Daniel Krakowczyk and Dilyara Bareeva and Franz Motzkus and Wojciech Samek and Sebastian Lapuschkin and Marina M.-C. Hohne},
	year         = 2023,
	journal      = {Journal of Machine Learning Research},
	volume       = 24,
	number       = 34,
	pages        = {1--11},
	doi          = {10.48550/arXiv.2202.06861}
}
@article{Herbinger.2024,
  author  = {Julia Herbinger and Marvin N. Wright and Thomas Nagler and Bernd Bischl and Giuseppe Casalicchio},
  title   = {Decomposing Global Feature Effects Based on Feature Interactions},
  journal = {Journal of Machine Learning Research},
  year    = {2024},
  volume  = {25},
  number  = {381},
  pages   = {1--65},
}
@article{Herren.2022,
	title        = {Statistical Aspects of SHAP: Functional ANOVA for Model Interpretation},
	author       = {Andrew Herren and P. Richard Hahn},
	year         = 2022,
	journal      = {CoRR},
	volume       = {abs/2208.09970},
	eprinttype   = {arXiv},
	eprint       = {2208.09970}
}
@inproceedings{Hiabu.2023,
	title        = {{Unifying local and global model explanations by functional decomposition of low dimensional structures}},
	author       = {Munir Hiabu and Joseph T. Meyer and Marvin N. Wright},
	year         = 2023,
	booktitle    = {Proceedings of the International Conference on Artificial Intelligence and Statistics {(AISTATS)}},
	pages        = {7040--7060}
}
@inproceedings{Hooker.2004,
	title        = {{Discovering additive structure in black box functions}},
	author       = {Giles Hooker},
	year         = 2004,
	booktitle    = {Proceedings of International Conference on Knowledge Discovery and Data Mining {(KDD)}},
	pages        = {575--580}
}
@article{Hooker.2007,
	title        = {{Generalized Functional ANOVA Diagnostics for High-Dimensional Functions of Dependent Variables}},
	author       = {Hooker, Giles},
	year         = 2007,
	journal      = {Journal of Computational and Graphical Statistics},
	publisher    = {Taylor \& Francis},
	volume       = 16,
	number       = 3,
	pages        = {709--732},
	doi          = {10.1198/106186007X237892}
}
@inproceedings{Hu.2014,
	title        = {Detecting Statistical Interaction Between Somatic Mutational Events and Germline Variation from Next-Generation Sequence Data},
	author       = {Hao Hu and Chad D. Huff},
	year         = 2014,
	booktitle    = {Biocomputing 2014: Proceedings of the Pacific Symposium},
	pages        = {51--62}
}
@inproceedings{Huang.2008,
	title        = {Statistical Analysis on Detecting Signals of Adverse Drug Events with Drug-Drug Interactions},
	author       = {Yu{-}Ting Huang and Shih{-}Fang Lin and Von{-}Wun Soo},
	year         = 2008,
	booktitle    = {International Conference on Bioinformatics \& Computational Biology, {(BIOCOMP 2008)}},
	publisher    = {{CSREA} Press},
	pages        = {970--976}
}
@article{Inglis.2022,
	title        = {{Visualizing Variable Importance and Variable Interaction Effects in Machine Learning Models}},
	author       = {Inglis, Alan and Parnell, Andrew and Hurley, Catherine B.},
	year         = 2022,
	journal      = {Journal of Computational and Graphical Statistics},
	publisher    = {Taylor \& Francis},
	volume       = 31,
	number       = 3,
	pages        = {766–778},
	doi          = {10.1080/10618600.2021.2007935}
}
@article{Islam.2022,
	title        = {A Systematic Review of Explainable Artificial Intelligence in Terms of Different Application Domains and Tasks},
	author       = {Islam, Mir Riyanul and Ahmed, Mobyen Uddin and Barua, Shaibal and Begum, Shahina},
	year         = 2022,
	journal      = {Applied Sciences},
	volume       = 12,
	number       = 3,
	doi          = {10.3390/app12031353}
}
@article{Janizek.2021,
	title        = {Explaining Explanations: Axiomatic Feature Interactions for Deep Networks},
	author       = {Joseph D. Janizek and Pascal Sturmfels and Su{-}In Lee},
	year         = 2021,
	journal      = {Journal of Machine Learning Research},
	volume       = 22,
	number       = 104,
	pages        = {1--54}
}
@inproceedings{Janzing.2020,
	title        = {{Feature relevance quantification in explainable AI: A causal problem}},
	author       = {Dominik Janzing and Lenon Minorics and Patrick Bl{\"{o}}baum},
	year         = 2020,
	booktitle    = {Proceedings of the International Conference on Artificial Intelligence and Statistics {(AISTATS)}},
	pages        = {2907--2916}
}
@inproceedings{Jethani.2022,
	title        = {{FastSHAP: Real-Time Shapley Value Estimation}},
	author       = {Neil Jethani and Mukund Sudarshan and Ian Connick Covert and Su{-}In Lee and Rajesh Ranganath},
	year         = 2022,
	booktitle    = {Proceedings of the International Conference on Learning Representations ({ICLR})}
}
@inproceedings{Jia.2019,
	title        = {Towards Efficient Data Valuation Based on the Shapley Value},
	author       = {Ruoxi Jia and David Dao and Boxin Wang and Frances Ann Hubis and Nick Hynes and Nezihe Merve G{\"{u}}rel and Bo Li and Ce Zhang and Dawn Song and Costas J. Spanos},
	year         = 2019,
	booktitle    = {Proceedings of the International Conference on Artificial Intelligence and Statistics {(AISTATS)}},
	pages        = {1167--1176}
}
@inproceedings{Jiang.2023,
	title        = {{OpenDataVal}: a Unified Benchmark for Data Valuation},
	author       = {Kevin Fu Jiang and Weixin Liang and James Zou and Yongchan Kwon},
	year         = 2023,
	booktitle    = {Proceedings of Advances in Neural Information Processing Systems {(NeurIPS)}}
}
@inproceedings{Jumelet.2023,
	title        = {Feature Interactions Reveal Linguistic Structure in Language Models},
	author       = {Jumelet, Jaap  and Zuidema, Willem},
	year         = 2023,
	booktitle    = {Findings of the Association for Computational Linguistics: ACL 2023},
	pages        = {8697--8712}
}
@inproceedings{Karczmarz.2022,
	title        = {{Improved feature importance computation for tree models based on the Banzhaf value}},
	author       = {Adam Karczmarz and Tomasz P. Michalak and Anish Mukherjee and Piotr Sankowski and Piotr Wygocki},
	year         = 2022,
	booktitle    = {Uncertainty in Artificial Intelligence, Proceedings of the Thirty-Eighth Conference on Uncertainty in Artificial Intelligence, ({UAI})},
	publisher    = {{PMLR}},
	series       = {Proceedings of Machine Learning Research},
	volume       = 180,
	pages        = {969--979}
}
@inproceedings{Ke.2017,
	title        = {{LightGBM: {A} Highly Efficient Gradient Boosting Decision Tree}},
	author       = {Guolin Ke and Qi Meng and Thomas Finley and Taifeng Wang and Wei Chen and Weidong Ma and Qiwei Ye and Tie{-}Yan Liu},
	year         = 2017,
	booktitle    = {Proceedings of Advances in Neural Information Processing Systems {(NeurIPS)}},
	pages        = {3146--3154}
}
@article{Kelley.1997,
	title        = {{Sparse spatial autoregressions}},
	author       = {R. {Kelley Pace} and Ronald Barry},
	year         = 1997,
	journal      = {Statistics \& Probability Letters},
	volume       = 33,
	number       = 3,
	pages        = {291--297},
	doi          = {10.1016/S0167-7152(96)00140-X}
}
@article{Klaise.2021,
	title        = {Alibi Explain: Algorithms for Explaining Machine Learning Models},
	author       = {Janis Klaise and Arnaud Van Looveren and Giovanni Vacanti and Alexandru Coca},
	year         = 2021,
	journal      = {Journal of Machine Learning Research},
	volume       = 22,
	number       = 181,
	pages        = {1--7}
}
@inproceedings{Kohavi.1996,
	title        = {Scaling up the Accuracy of Naive-Bayes Classifiers: A Decision-Tree Hybrid},
	author       = {Kohavi, R.},
	year         = 1996,
	booktitle    = {Proceedings of International Conference on Knowledge Discovery and Data Mining {(KDD)}},
	pages        = {202--207}
}
@article{Kokhlikyan.2020,
	title        = {Captum: A unified and generic model interpretability library for {PyTorch}},
	author       = {Narine Kokhlikyan and Vivek Miglani and Miguel Martin and Edward Wang and Bilal Alsallakh and Jonathan Reynolds and Alexander Melnikov and Natalia Kliushkina and Carlos Araya and Siqi Yan and Orion Reblitz-Richardson},
	year         = 2020,
	journal      = {arXiv preprint arXiv:2009.07896},
	doi          = {10.48550/arXiv.2009.07896}
}
@inproceedings{Kolpaczki.2024a,
	title        = {Approximating the Shapley Value without Marginal Contributions},
	author       = {Patrick Kolpaczki and Viktor Bengs and Maximilian Muschalik and Eyke H{\"{u}}llermeier},
	year         = 2024,
	booktitle    = {Proceeedings of the {AAAI} Conference on Artificial Intelligence {(AAAI)}},
	pages        = {13246--13255}
}
@inproceedings{Kolpaczki.2024b,
	title        = {{SVARM-IQ:} Efficient Approximation of Any-order Shapley Interactions through Stratification},
	author       = {Patrick Kolpaczki and Maximilian Muschalik and Fabian Fumagalli and Barbara Hammer and Eyke H{\"{u}}llermeier},
	year         = 2024,
	booktitle    = {Proceedings of the International Conference on Artificial Intelligence and Statistics {(AISTATS)}},
	pages        = {3520--3528},
keywords = {FF},
}
@inproceedings{Kolpaczki.2024c,
	title        = {How Much Can Stratification Improve the Approximation of Shapley Values?},
	author       = {Patrick Kolpaczki and Georg Haselbeck and Eyke H{\"{u}}llermeier},
	year         = 2024,
	booktitle    = {Proceeedings of World Conference on Explainable Artificial Intelligence {(xAI)}},
	pages        = {489--512}
}
@inproceedings{Kumar.2020,
	title        = {{Problems with Shapley-value-based explanations as feature importance measures}},
	author       = {I. Elizabeth Kumar and Suresh Venkatasubramanian and Carlos Scheidegger and Sorelle A. Friedler},
	year         = 2020,
	booktitle    = {Proceedings of the International Conference on Machine Learning {(ICML)}},
	pages        = {5491--5500}
}
@inproceedings{Kumar.2021,
	title        = {{Shapley Residuals: Quantifying the limits of the Shapley value for explanations}},
	author       = {Indra Kumar and Carlos Scheidegger and Suresh Venkatasubramanian and Sorelle A. Friedler},
	year         = 2021,
	booktitle    = {Proceedings of Advances in Neural Information Processing Systems {(NeurIPS)}},
	pages        = {26598--26608}
}
@inproceedings{Kwon.2022,
	title        = {WeightedSHAP: analyzing and improving Shapley based feature attributions},
	author       = {Yongchan Kwon and James Y. Zou},
	year         = 2022,
	booktitle    = {Proceedings of Advances in Neural Information Processing Systems {(NeurIPS)}}
}
@inproceedings{Lengerich.2020,
	title        = {{Purifying Interaction Effects with the Functional ANOVA: An Efficient Algorithm for Recovering Identifiable Additive Models}},
	author       = {Benjamin J. Lengerich and Sarah Tan and Chun{-}Hao Chang and Giles Hooker and Rich Caruana},
	year         = 2020,
	booktitle    = {Proceedings of the International Conference on Artificial Intelligence and Statistics {(AISTATS)}},
	pages        = {2402--2412}
}
@inproceedings{Lhoest.2021,
	title        = {Datasets: A Community Library for Natural Language Processing},
	author       = {Lhoest, Quentin and Villanova del Moral, Albert and von Platen, Patrick and Wolf, Thomas and Šaško, Mario and Jernite, Yacine and Thakur, Abhishek and Tunstall, Lewis and Patil, Suraj and Drame, Mariama and Chaumond, Julien and Plu, Julien and Davison, Joe and Brandeis, Simon and Sanh, Victor and Le Scao, Teven and Canwen Xu, Kevin and Patry, Nicolas and Liu, Steven and McMillan-Major, Angelina and Schmid, Philipp and Gugger, Sylvain and Raw, Nathan and Lesage, Sylvain and Lozhkov, Anton and Carrigan, Matthew and Matussière, Théo and von Werra, Leandro and Debut, Lysandre and Bekman, Stas and Delangue, Clément},
	year         = 2021,
	booktitle    = {Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing: System Demonstrations, {(EMNLP 2021)}},
	publisher    = {Association for Computational Linguistics},
	pages        = {175--184}
}
@inproceedings{Li.2023,
	title        = {$\mathcal{M}^4$: A Unified {XAI} Benchmark for Faithfulness Evaluation of Feature Attribution Methods across Metrics, Modalities and Models},
	author       = {Li, Xuhong and Du, Mengnan and Chen, Jiamin and Chai, Yekun and Lakkaraju, Himabindu and Xiong, Haoyi},
	year         = 2023,
	booktitle    = {Proceedings of Advances in Neural Information Processing Systems {(NeurIPS)}}
}
@inproceedings{Li.2024,
	title        = {Faster Approximation of Probabilistic and Distributional Values via Least Squares},
	author       = {Weida Li and Yaoliang Yu},
	year         = 2024,
	booktitle    = {Proceedings of the International Conference on Learning Representations {(ICLR)}}
}
@inproceedings{Lin.2023,
	title        = {On the Robustness of Removal-Based Feature Attributions},
	author       = {Chris Lin and Ian Covert and Su-In Lee},
	year         = 2023,
	booktitle    = {Proceedings of Advances in Neural Information Processing Systems {(NeurIPS)}}
}
@article{Liu.2019,
	title        = {Visualizing complex feature interactions and feature sharing in genomic deep neural networks},
	author       = {Ge Liu and Haoyang Zeng and David K. Gifford},
	year         = 2019,
	journal      = {{BMC} Bioinformatics},
	volume       = 20,
	number       = 1,
	pages        = {401:1--401:14},
	doi          = {10.1186/s12859-019-2957-4}
}
@inproceedings{Liu.2021,
	title        = {Synthetic Benchmarks for Scientific Research in Explainable Machine Learning},
	author       = {Liu, Yang and Khandagale, Sujay and White, Colin and Neiswanger, Willie},
	year         = 2021,
	booktitle    = {Proceedings of Advances in Neural Information Processing Systems {(NeurIPS)}}
}
@article{Loh.2022,
	title        = {Application of explainable artificial intelligence for healthcare: {A} systematic review of the last decade {(2011-2022)}},
	author       = {Hui Wen Loh and Chui Ping Ooi and Silvia Seoni and Prabal Datta Barua and Filippo Molinari and U. Rajendra Acharya},
	year         = 2022,
	journal      = {Computer Methods and Programs in Biomedicine},
	volume       = 226,
	pages        = 107161,
	doi          = {10.1016/J.CMPB.2022.107161}
}
@inproceedings{Lou.2013,
	title        = {Accurate intelligible models with pairwise interactions},
	author       = {Yin Lou and Rich Caruana and Johannes Gehrke and Giles Hooker},
	year         = 2013,
	booktitle    = {Proceedings of International Conference on Knowledge Discovery and Data Mining {(KDD)}},
	pages        = {623--631}
}
@inproceedings{Lundberg.2017,
	title        = {{A Unified Approach to Interpreting Model Predictions}},
	author       = {Scott M. Lundberg and Su{-}In Lee},
	year         = 2017,
	booktitle    = {Proceedings of Advances in Neural Information Processing Systems {(NeurIPS)}},
	pages        = {4765--4774}
}
@article{Lundberg.2020,
	title        = {{From local explanations to global understanding with explainable AI for trees}},
	author       = {Scott M. Lundberg and Gabriel G. Erion and Hugh Chen and Alex J. DeGrave and Jordan M. Prutkin and Bala Nair and Ronit Katz and Jonathan Himmelfarb and Nisha Bansal and Su{-}In Lee},
	year         = 2020,
	journal      = {Nature Machine Intelligence},
	volume       = 2,
	number       = 1,
	pages        = {56--67},
	doi          = {10.1038/s42256-019-0138-9}
}
@article{Lunetta.2004,
	title        = {{Screening large-scale association study data: exploiting interactions using random forests}},
	author       = {Lunetta, Kathryn L and Hayward, L Brooke and Segal, Jonathan and Van Eerdewegh, Paul},
	year         = 2004,
	journal      = {BMC Genetics},
	volume       = 5,
	pages        = 32,
	doi          = {10.1186/1471-2156-5-32}
}
@inproceedings{Maas.2011,
	title        = {Learning Word Vectors for Sentiment Analysis},
	author       = {Maas, Andrew L.  and  Daly, Raymond E.  and  Pham, Peter T.  and  Huang, Dan  and  Ng, Andrew Y.  and  Potts, Christopher},
	year         = 2011,
	booktitle    = {Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies {(HLT)}},
	pages        = {142--150}
}
@article{Maleki.2013,
	title        = {Bounding the Estimation Error of Sampling-based Shapley Value Approximation With/Without Stratifying},
	author       = {Sasan Maleki and Long Tran{-}Thanh and Greg Hines and Talal Rahwan and Alex Rogers},
	year         = 2013,
	journal      = {CoRR},
	volume       = {abs/1306.4265},
	eprinttype   = {arXiv},
	eprint       = {1306.4265}
}
@article{Marichal_2000,
	title        = {The influence of variables on pseudo-Boolean functions with applications to game theory and multicriteria decision making},
	author       = {Marichal, Jean-Luc},
	year         = 2000,
	journal      = {Discrete Applied Mathematics},
	volume       = 107,
	pages        = {139--164}
}
@inbook{Marichal.1999,
	title        = {The Chaining Interaction Index among Players in Cooperative Games},
	author       = {Marichal, Jean-Luc and Roubens, Marc},
	year         = 1999,
	booktitle    = {Advances in Decision Analysis},
	publisher    = {Springer Netherlands},
	pages        = {69–85},
	doi          = {10.1007/978-94-017-0647-6_5}
}
@inproceedings{Miglani.2023,
	title        = {Using Captum to Explain Generative Language Models},
	author       = {Miglani, Vivek and Yang, Aobo and Markosyan, Aram and Garcia-Olano, Diego and Kokhlikyan, Narine},
	year         = 2023,
	booktitle    = {Proceedings of the 3rd Workshop for Natural Language Processing Open Source Software},
	publisher    = {Empirical Methods in Natural Language Processing},
	doi          = {10.18653/v1/2023.nlposs-1.19},
	url          = {http://dx.doi.org/10.18653/v1/2023.nlposs-1.19}
}
@inproceedings{Molnar.2019,
	title        = {Quantifying Model Complexity via Functional Decomposition for Better Post-hoc Interpretability},
	author       = {Christoph Molnar and Giuseppe Casalicchio and Bernd Bischl},
	year         = 2019,
	booktitle    = {Machine Learning and Knowledge Discovery in Databases - International Workshops of ECML PKDD},
	pages        = {193--204},
	doi          = {10.1007/978-3-030-43823-4_17}
}
@inproceedings{Moro.2011,
	title        = {{Using Data Mining for Bank Direct Marketing: An Application of the CRISP-DM Methodology}},
	author       = {Moro, S. and Cortez, P. and Laureano, R.},
	year         = 2011,
	booktitle    = {Proceedings of the European Simulation and Modelling Conference (ESM 2011)}
}
@inproceedings{Murdoch.2018,
	title        = {Beyond Word Importance: Contextual Decomposition to Extract Interactions from LSTMs},
	author       = {W. James Murdoch and Peter J. Liu and Bin Yu},
	year         = 2018,
	booktitle    = {Proceedings of the International Conference on Learning Representations ({ICLR})}
}
@inproceedings{Muschalik.2024,
	title        = {{Beyond TreeSHAP: Efficient Computation of Any-Order Shapley Interactions for Tree Ensembles}},
	author       = {Maximilian Muschalik and Fabian Fumagalli and Barbara Hammer and Eyke H{\"{u}}llermeier},
	year         = 2024,
	booktitle    = {Proceeedings of the {AAAI} Conference on Artificial Intelligence {(AAAI)}},
	pages        = {14388--14396},
    doi = {10.1609/aaai.v38i13.29352},
keywords = {FF},
}
@article{Nori.2019,
	title        = {{InterpretML}: A unified framework for machine learning interpretability},
	author       = {Nori, Harsha and Jenkins, Samuel and Koch, Paul and Caruana, Rich},
    journal      = {CoRR},
    volume       = {abs/1909.09223},
    year         = {2019},
    eprinttype    = {arXiv},
    eprint       = {1909.09223},
}
@article{Olsen.2022,
	title        = {Using {Shapley} Values and Variational Autoencoders to Explain Predictive Models with Dependent Mixed Features},
	author       = {Lars H. B. Olsen and Ingrid K. Glad and Martin Jullum and Kjersti Aas},
	year         = 2022,
	journal      = {Journal of Machine Learning Research},
	volume       = 23,
	number       = 213,
	pages        = {1--51}
}
@article{Olsen.2024,
	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},
	year         = 2024,
	journal      = {Data Mining and Knowledge Discovery},
	pages        = {1--48}
}
@article{Owen.2014,
	title        = {Sobol' Indices and Shapley Value},
	author       = {Art B. Owen},
	year         = 2014,
	journal      = {{SIAM/ASA} Journal for Uncertainty Quantification},
	volume       = 2,
	number       = 1,
	pages        = {245--251},
	doi          = {10.1137/130936233}
}
@article{Owen.2017,
	title        = {On Shapley Value for Measuring Importance of Dependent Inputs},
	author       = {Art B. Owen and Cl{\'{e}}mentine Prieur},
	year         = 2017,
	journal      = {{SIAM/ASA} Journal for Uncertainty Quantification},
	volume       = 5,
	number       = 1,
	pages        = {986--1002},
	doi          = {10.1137/16M1097717}
}
@inproceedings{Paszke.2017,
	title        = {PyTorch: An Imperative Style, High-Performance Deep Learning Library},
	author       = {Paszke, Adam and Gross, Sam and Massa, Francisco and Lerer, Adam and Bradbury, James and Chanan, Gregory and Killeen, Trevor and Lin, Zeming and Gimelshein, Natalia and Antiga, Luca and Desmaison, Alban and Kopf, Andreas and Yang, Edward and DeVito, Zachary and Raison, Martin and Tejani, Alykhan and Chilamkurthy, Sasank and Steiner, Benoit and Fang, Lu and Bai, Junjie and Chintala, Soumith},
	year         = 2019,
	booktitle    = {Proceedings of Advances in Neural Information Processing Systems {(NeurIPS)}},
	pages        = {8024--8035}
}
@inproceedings{Patel.2021,
	title        = {High Dimensional Model Explanations: An Axiomatic Approach},
	author       = {Neel Patel and Martin Strobel and Yair Zick},
	year         = 2021,
	booktitle    = {Proceedings of {ACM} Conference on Fairness, Accountability, and Transparency, Virtual Event ({FAccT})},
	pages        = {401--411}
}
@article{Pedregosa.2011,
	title        = {Scikit-learn: Machine Learning in Python},
	author       = {Fabian Pedregosa and Ga{\"{e}}l Varoquaux and Alexandre Gramfort and Vincent Michel and Bertrand Thirion and Olivier Grisel and Mathieu Blondel and Peter Prettenhofer and Ron Weiss and Vincent Dubourg and Jake VanderPlas and Alexandre Passos and David Cournapeau and Matthieu Brucher and Matthieu Perrot and Edouard Duchesnay},
	year         = 2011,
	journal      = {Journal of Machine Learning Research},
	volume       = 12,
	pages        = {2825--2830},
	doi          = {10.5555/1953048.2078195}
}
@article{Pelegrina.2023,
	title        = {A \emph{k}-additive Choquet integral-based approach to approximate the {SHAP} values for local interpretability in machine learning},
	author       = {Guilherme Dean Pelegrina and Leonardo Tomazeli Duarte and Michel Grabisch},
	year         = 2023,
	journal      = {Artificial Intelligence},
	volume       = 325,
	pages        = 104014,
	doi          = {10.1016/J.ARTINT.2023.104014}
}
@inproceedings{Pfannschmidt.2016,
	title        = {Evaluating Tests in Medical Diagnosis: Combining Machine Learning with Game-Theoretical Concepts},
	author       = {Karlson Pfannschmidt and Eyke H{\"{u}}llermeier and Susanne Held and Reto Neiger},
	year         = 2016,
	booktitle    = {Proceedings of Information Processing and Management of Uncertainty in Knowledge-Based Systems {(IPMU)}},
	pages        = {450--461}
}
@inproceedings{PoursabziSangdeh.2021,
	title        = {Manipulating and Measuring Model Interpretability},
	author       = {Forough Poursabzi-Sangdeh and Daniel G. Goldstein and Jake M. Hofman and Jennifer Wortman Vaughan and Hanna Wallach},
	year         = 2021,
	booktitle    = {Proceedings of {CHI} Conference on Human Factors in Computing Systems {(CHI)}}
}
@article{Rabitti.2019,
	title        = {A Shapley-Owen Index for Interaction Quantification},
	author       = {Rabitti, Giovanni and Borgonovo, Emanuele},
	year         = 2019,
	journal      = {SIAM/ASA Journal on Uncertainty Quantification},
	volume       = 7,
	number       = 3,
	pages        = {1060–1075},
	doi          = {10.1137/18M1221801}
}
@article{Ravina.2021,
	title        = {{Distilling Interpretable Models into Human-Readable Code}},
	author       = {Walker Ravina and Ethan Sterling and Olexiy Oryeshko and Nathan Bell and Honglei Zhuang and Xuanhui Wang and Yonghui Wu and Alexander Grushetsky},
	year         = 2021,
	journal      = {CoRR},
	volume       = {abs/2101.08393}
}
@article{Rong.2024,
	title        = {Towards Human-Centered Explainable {AI}: A Survey of User Studies for Model Explanations},
	author       = {Rong, Yao and Leemann, Tobias and Nguyen, Thai-Trang and Fiedler, Lisa and Qian, Peizhu and Unhelkar, Vaibhav and Seidel, Tina and Kasneci, Gjergji and Kasneci, Enkelejda},
	year         = 2024,
	journal      = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
	volume       = 46,
	number       = 4,
	pages        = {2104--2122}
}
@incollection{Rota.1964,
	title        = {On the foundations of combinatorial theory: I. Theory of M{\"o}bius functions},
	author       = {Rota, Gian-Carlo},
	year         = 1964,
	booktitle    = {Classic Papers in Combinatorics},
	publisher    = {Springer},
	pages        = {332--360}
}
@inproceedings{Rozemberczki.2021,
	title        = {The Shapley Value of Classifiers in Ensemble Games},
	author       = {Benedek Rozemberczki and Rik Sarkar},
	year         = 2021,
	booktitle    = {Proceedings of the International Conference on Information and Knowledge Management {(CIKM)}},
	pages        = {1558--1567}
}
@article{Ruiz.1996,
	title        = {The least square prenucleolus and the least square nucleolus. Two values for TU games based on the excess vector},
	author       = {Ruiz, Luis M. and Valenciano, Federico and Zarzuelo, Jose M.},
	year         = 1996,
	journal      = {International Journal of Game Theory},
	volume       = 25,
	number       = 1,
	pages        = {113–134},
	doi          = {10.1007/BF01254388}
}
@article{Ruiz.1998,
	title        = {The Family of Least Square Values for Transferable Utility Games},
	author       = {Ruiz, Luis M. and Valenciano, Federico and Zarzuelo, Jose M.},
	year         = 1998,
	journal      = {Games and Economic Behavior},
	volume       = 24,
	number       = 1,
	pages        = {109–130},
	doi          = {10.1006/game.1997.0622}
}
@inproceedings{Rundel.2024,
	title        = {Interpretable Machine Learning for TabPFN},
	author       = {David Rundel and Julius Kobialka and Constantin von Crailsheim and Matthias Feurer and Thomas Nagler and David R{\"{u}}gamer},
	year         = 2024,
	booktitle    = {Proceeedings of World Conference on Explainable Artificial Intelligence {(xAI)}},
	publisher    = {Springer},
	series       = {Communications in Computer and Information Science},
	volume       = 2154,
	pages        = {465--476},
	doi          = {10.1007/978-3-031-63797-1\_23},
	editor       = {Luca Longo and Sebastian Lapuschkin and Christin Seifert}
}
@article{Samek.2021,
	title        = {Explaining Deep Neural Networks and Beyond: {A} Review of Methods and Applications},
	author       = {Wojciech Samek and Gr{\'{e}}goire Montavon and Sebastian Lapuschkin and Christopher J. Anders and Klaus{-}Robert M{\"{u}}ller},
	year         = 2021,
	journal      = {Proceedings of the {IEEE}},
	volume       = 109,
	number       = 3,
	pages        = {247--278},
	doi          = {10.1109/JPROC.2021.3060483}
}
@article{Sanh.2019,
	title        = {DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter},
	author       = {Victor Sanh and Lysandre Debut and Julien Chaumond and Thomas Wolf},
	year         = 2019,
	journal      = {CoRR},
	volume       = {abs/1910.01108},
	eprinttype   = {arXiv},
	eprint       = {1910.01108}
}
@incollection{Shapley.1953,
	title        = {{A Value for n-Person Games}},
	author       = {Shapley, L. S.},
	year         = 1953,
	booktitle    = {Contributions to the Theory of Games (AM-28), Volume II},
	publisher    = {Princeton University Press},
	pages        = {307--318}
}
@article{ShwartzZiv.2022,
	title        = {{Tabular data: Deep learning is not all you need}},
	author       = {Ravid Shwartz{-}Ziv and Amitai Armon},
	year         = 2022,
	journal      = {Information Fusion},
	volume       = 81,
	pages        = {84--90},
	doi          = {10.1016/j.inffus.2021.11.011}
}
@inproceedings{Singh.2019,
	title        = {Hierarchical interpretations for neural network predictions},
	author       = {Chandan Singh and W. James Murdoch and Bin Yu},
	year         = 2019,
	booktitle    = {Proceedings of the International Conference on Learning Representations ({ICLR})}
}
@inproceedings{Slack.2020,
	title        = {Fooling {LIME} and {SHAP:} Adversarial Attacks on Post hoc Explanation Methods},
	author       = {Dylan Slack and Sophie Hilgard and Emily Jia and Sameer Singh and Himabindu Lakkaraju},
	year         = 2020,
	booktitle    = {Proceedings of the {AAAI/ACM} Conference on AI, Ethics, and Society ({AIES})},
	pages        = {180--186}
}
@article{Slack.2023,
	title        = {Explaining machine learning models with interactive natural language conversations using TalkToModel},
	author       = {Slack, Dylan and Krishna, Satyapriya and Lakkaraju, Himabindu and Singh, Sameer},
	year         = 2023,
	journal      = {Nature Machine Intelligence},
	volume       = 5,
	number       = 8,
	pages        = {873–883},
	doi          = {10.1038/s42256-023-00692-8}
}
@inproceedings{Sorokina.2008,
	title        = {Detecting statistical interactions with additive groves of trees},
	author       = {Daria Sorokina and Rich Caruana and Mirek Riedewald and Daniel Fink},
	year         = 2008,
	booktitle    = {Proceedings of the International Conference on Machine Learning {(ICML)}},
	pages        = {1000--1007}
}
@article{Spivey.2007,
	title        = {Combinatorial sums and finite differences},
	author       = {Michael Z. Spivey},
	year         = 2007,
	journal      = {Discrete Mathematics},
	volume       = 307,
	number       = 24,
	pages        = {3130--3146},
	doi          = {10.1016/j.disc.2007.03.052}
}
@article{Strumbelj.2014,
	title        = {{Explaining prediction models and individual predictions with feature contributions}},
	author       = {Erik Strumbelj and Igor Kononenko},
	year         = 2014,
	journal      = {Knowledge and Information Systems},
	volume       = 41,
	number       = 3,
	pages        = {647--665},
	doi          = {10.1007/s10115-013-0679-x}
}
@inproceedings{Sundararajan.2020,
	title        = {{The Shapley Taylor Interaction Index}},
	author       = {Mukund Sundararajan and Kedar Dhamdhere and Ashish Agarwal},
	year         = 2020,
	booktitle    = {Proceedings of the International Conference on Machine Learning {(ICML)}},
	pages        = {9259--9268}
}
@inproceedings{Sundararajan.2020b,
	title        = {{The Many Shapley Values for Model Explanation}},
	author       = {Mukund Sundararajan and Amir Najmi},
	year         = 2020,
	booktitle    = {Proceedings of the International Conference on Machine Learning {(ICML)}},
	pages        = {9269--9278}
}
@article{Hollmann.2025,
	title        = {Accurate predictions on small data with a tabular foundation model},
	author       = {Noah Hollmann and Samuel Müller and Lennart Purucker and Arjun Krishnakumar and Max Körfer and Shi Bin Hoo and Robin Tibor Schirrmeister and Frank Hutter},
	year         = 2025,
	journal      = {Nature},
	volume       = 637,
	number       = 8045,
	pages        = {319--326},
	doi          = {10.1038/s41586-024-08328-6}
}
@inproceedings{Tay.2022,
	title        = {Incentivizing Collaboration in Machine Learning via Synthetic Data Rewards},
	author       = {Sebastian Shenghong Tay and Xinyi Xu and Chuan Sheng Foo and Bryan Kian Hsiang Low},
	year         = 2022,
	booktitle    = {Proceeedings of the {AAAI} Conference on Artificial Intelligence {(AAAI)}},
	pages        = {9448--9456}
}
@article{Tokdar.2020,
	title        = {Importance Sampling: A Review},
	author       = {Tokdar, Surya T and Kass, Robert E},
	year         = 2010,
	journal      = {Wiley Interdisciplinary Reviews: Computational Statistics},
	publisher    = {Wiley Online Library},
	volume       = 2,
	number       = 1,
	pages        = {54--60}
}
@article{Tsai.2022,
	title        = {{Faith-Shap: The Faithful Shapley Interaction Index}},
	author       = {Che{-}Ping Tsai and Chih{-}Kuan Yeh and Pradeep Ravikumar},
	year         = 2023,
	journal      = {Journal of Machine Learning Research},
	volume       = 24,
	number       = 94,
	pages        = {1--42}
}
@inproceedings{Tsang.2018,
	title        = {Detecting Statistical Interactions from Neural Network Weights},
	author       = {Michael Tsang and Dehua Cheng and Yan Liu},
	year         = 2018,
	booktitle    = {Proceedings of the International Conference on Learning Representations ({ICLR})}
}
@inproceedings{Tsang.2020,
	title        = {{How does This Interaction Affect Me? Interpretable Attribution for Feature Interactions}},
	author       = {Tsang, Michael and Rambhatla, Sirisha and Liu, Yan},
	year         = 2020,
	booktitle    = {Proceedings of Advances in Neural Information Processing Systems {(NeurIPS)}},
	pages        = {6147--6159}
}
@inproceedings{Tsang.2020b,
	title        = {Feature Interaction Interpretability: A Case for Explaining Ad-Recommendation Systems via Neural Interaction Detection},
	author       = {Michael Tsang and Dehua Cheng and Hanpeng Liu and Xue Feng and Eric Zhou and Yan Liu},
	year         = 2020,
	booktitle    = {Proceedings of the International Conference on Learning Representations ({ICLR})}
}
@article{vanCampen.2018,
	title        = {A new approximation method for the Shapley value applied to the {WTC} 9/11 terrorist attack},
	author       = {Tjeerd van Campen and Herbert Hamers and Bart Husslage and Roy Lindelauf},
	year         = 2018,
	journal      = {Social Network Analysis and Mining},
	volume       = 8,
	number       = 1,
	pages        = {3:1--3:12}
}
@inproceedings{Watson.2023,
	title        = {Explaining Predictive Uncertainty with Information Theoretic Shapley Values},
	author       = {David S. Watson and Joshua O'Hara and Niek Tax and Richard Mudd and Ido Guy},
	year         = 2023,
	booktitle    = {Proceedings of Advances in Neural Information Processing Systems {(NeurIPS)}}
}
@article{Welford.1962,
	title        = {Note on a Method for Calculating Corrected Sums of Squares and Products},
	author       = {Welford, B. P.},
	year         = 1962,
	journal      = {Technometrics},
	publisher    = {Taylor {\&}  Francis},
	volume       = 4,
	number       = 3,
	pages        = {419–420},
	doi          = {10.1080/00401706.1962.10490022}
}
@inproceedings{Williamson.2020,
	title        = {Efficient nonparametric statistical inference on population feature importance using Shapley values},
	author       = {Brian D. Williamson and Jean Feng},
	year         = 2020,
	booktitle    = {Proceedings of the International Conference on Machine Learning {(ICML)}},
	pages        = {10282--10291}
}
@article{Winham.2012,
	title        = {{SNP interaction detection with Random Forests in high-dimensional genetic data}},
	author       = {Winham, Stacey J and Colby, Colin L and Freimuth, Robert R and Wang, Xiao and de Andrade, Mariza and Huebner, Mathias and Biernacka, Joanna M},
	year         = 2012,
	journal      = {BMC Bioinformatics},
	volume       = 13,
	pages        = 164,
	doi          = {10.1186/1471-2105-13-164}
}
@inproceedings{Wolf.2020,
	title        = {Transformers: State-of-the-Art Natural Language Processing},
	author       = {Thomas Wolf and Lysandre Debut and Victor Sanh and Julien Chaumond and Clement Delangue and Anthony Moi and Pierric Cistac and Tim Rault and R{\'{e}}mi Louf and Morgan Funtowicz and Joe Davison and Sam Shleifer and Patrick von Platen and Clara Ma and Yacine Jernite and Julien Plu and Canwen Xu and Teven Le Scao and Sylvain Gugger and Mariama Drame and Quentin Lhoest and Alexander M. Rush},
	year         = 2020,
	booktitle    = {Proceedings of the Conference on Empirical Methods in Natural Language Processing: System Demonstrations {(EMNLP)}},
	pages        = {38--45}
}
@article{Wright.2016,
	title        = {Do little interactions get lost in dark random forests?},
	author       = {Marvin N. Wright and Andreas Ziegler and Inke R. K{\"{o}}nig},
	year         = 2016,
	journal      = {{BMC} Bioinformatics},
	volume       = 17,
	pages        = 145,
	doi          = {10.1186/s12859-016-0995-8}
}
@article{Yang.2021,
	title        = {{Fast TreeSHAP: Accelerating SHAP Value Computation for Trees}},
	author       = {Jilei Yang},
	year         = 2021,
	journal      = {CoRR},
	eprint       = {abs/2109.09847}
}
@inproceedings{Yeh.2018,
	title        = {Representer Point Selection for Explaining Deep Neural Networks},
	author       = {Chih{-}Kuan Yeh and Joon Sik Kim and Ian En{-}Hsu Yen and Pradeep Ravikumar},
	year         = 2018,
	booktitle    = {Proceedings of Advances in Neural Information Processing Systems {(NeurIPS)}},
	pages        = {9311--9321}
}
@inproceedings{Yu.2022,
	title        = {{Linear tree shap}},
	author       = {Peng Yu and Albert Bifet and Jesse Read and Chao Xu},
	year         = 2022,
	booktitle    = {Proceedings of Advances in Neural Information Processing Systems {(NeurIPS)}}
}
@inproceedings{Zern.2023,
	title        = {Interventional {SHAP} Values and Interaction Values for Piecewise Linear Regression Trees},
	author       = {Artjom Zern and Klaus Broelemann and Gjergji Kasneci},
	year         = 2023,
	booktitle    = {Proceeedings of the {AAAI} Conference on Artificial Intelligence {(AAAI)}},
	pages        = {11164--11173}
}
@inproceedings{Zhang.2021,
	title        = {{Interpreting Multivariate Shapley Interactions in DNNs}},
	author       = {Hao Zhang and Yichen Xie and Longjie Zheng and Die Zhang and Quanshi Zhang},
	year         = 2021,
	booktitle    = {Proceeedings of the {AAAI} Conference on Artificial Intelligence {(AAAI)}},
	pages        = {10877--10886}
}
@article{Zhang.2022,
	title        = {Applications of Explainable Artificial Intelligence in Diagnosis and Surgery},
	author       = {Zhang, Yiming and Weng, Ying and Lund, Jonathan},
	year         = 2022,
	journal      = {Diagnostics},
	volume       = 12,
	number       = 2,
	doi          = {10.3390/diagnostics12020237}
}
@inproceedings{Zhou.2023,
	title        = {Probably Approximate Shapley Fairness with Applications in Machine Learning},
	author       = {Zijian Zhou and Xinyi Xu and Rachael Hwee Ling Sim and Chuan Sheng Foo and Bryan Kian Hsiang Low},
	year         = 2023,
	booktitle    = {Proceeedings of the {AAAI} Conference on Artificial Intelligence {(AAAI)}},
	pages        = {5910--5918}
}
@inproceedings{Fumagalli.2024a,
      title={{Unifying Feature-Based Explanations with Functional ANOVA and Cooperative Game Theory}}, 
      author={Fabian Fumagalli and Maximilian Muschalik and Eyke Hüllermeier and Barbara Hammer and Julia Herbinger},
      year={2025},
	   booktitle = {Proceedings of the International Conference on Artificial Intelligence and Statistics {(AISTATS)}},
        pages = {5140--5148},
        keywords = {FF},
}
@inproceedings{Muschalik.2024a,
  title     = {{shapiq: Shapley Interactions for Machine Learning}},
  author    = {Maximilian Muschalik and Hubert Baniecki and Fabian Fumagalli   and Patrick Kolpaczki and Barbara Hammer and Eyke H\"{u}llermeier},
	booktitle    = {Proceedings of Advances in Neural Information Processing Systems {(NeurIPS)}},
  year      = {2024},
 pages = {130324--130357},
keywords = {FF},
}
@inproceedings{
Spliethoever.2025,
    title = "Adaptive Prompting: Ad-hoc Prompt Composition for Social Bias Detection",
    author = {Splieth{\"o}ver, Maximilian  and
      Knebler, Tim  and
      Fumagalli, Fabian  and
      Muschalik, Maximilian  and
      Hammer, Barbara  and
      H{\"u}llermeier, Eyke  and
      Wachsmuth, Henning},
    booktitle = "Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics (NAACL)",
    year = "2025",
    doi = "10.18653/v1/2025.naacl-long.122",
    pages = "2421--2449",
    keywords = {FF},
}

@inproceedings{
Visser.2025,
title={{Explaining Outliers using Isolation Forest and Shapley Interactions}},
author={Roel Visser and Fabian Fumagalli and Eyke Hüllermeier and Barbara Hammer},
booktitle={Proceedings of the European Symposium on Artificial Neural Networks {(ESANN)}},
year={2025},
keywords = {FF},
}

@inproceedings{Kenneweg.2024,
  author       = {Philip Kenneweg and
                  Tristan Kenneweg and
                  Fabian Fumagalli and
                  Barbara Hammer},
  title        = {{No learning rates needed: Introducing {SALSA} - Stable Armijo Line Search Adaptation}},
  booktitle    = {Proceedings of the International Joint Conference on Neural Networks {(IJCNN)}},
  year         = {2024},
  doi          = {10.1109/IJCNN60899.2024.10650124},
keywords = {FF},
}
@article{Fumagalli.2023a,
  author       = {Fabian Fumagalli and
                  Maximilian Muschalik and
                  Eyke H{\"{u}}llermeier and
                  Barbara Hammer},
  title        = {{Incremental permutation feature importance (iPFI): towards online explanations on data streams}},
  journal      = {Machine Learning},
  volume       = {112},
  number       = {12},
  pages        = {4863--4903},
  year         = {2023},
  doi          = {10.1007/S10994-023-06385-Y},
keywords = {FF},
}
@inproceedings{Muschalik.2023,
  author       = {Maximilian Muschalik and
                  Fabian Fumagalli and
                  Barbara Hammer and
                  Eyke H{\"{u}}llermeier},
  title        = {{iSAGE: An Incremental Version of SAGE for Online Explanation on Data Streams}},
  booktitle    = {Machine Learning and Knowledge Discovery in Databases: Research Track - European Conference ({ECML} {PKDD})},
  volume       = {14171},
  pages        = {428--445},
  year         = {2023},
  doi          = {10.1007/978-3-031-43418-1_26},
keywords = {FF},
}
@inproceedings{Fumagalli.2023b,
  author       = {Fabian Fumagalli and
                  Maximilian Muschalik and
                  Eyke H{\"{u}}llermeier and
                  Barbara Hammer},
  title        = {{On Feature Removal for Explainability in Dynamic Environments}},
  booktitle    = {Proceedings of the European Symposium on Artificial Neural Networks ({ESANN})},
  year         = {2023},
  doi          = {10.14428/ESANN/2023.ES2023-148},
keywords = {FF},
}
@inproceedings{Muschalik.2023a,
  author       = {Maximilian Muschalik and
                  Fabian Fumagalli and
                  Rohit Jagtani and
                  Barbara Hammer and
                  Eyke H{\"{u}}llermeier},
  title        = {{iPDP: On Partial Dependence Plots in Dynamic Modeling Scenarios}},
  booktitle    = {Proceeedings of World Conference on Explainable Artificial Intelligence {(xAI)}},
  year         = {2023},
  doi = {10.1007/978-3-031-44064-9_11},
keywords = {FF},
}
@article{Muschalik.2022,
  author       = {Maximilian Muschalik and
                  Fabian Fumagalli and
                  Barbara Hammer and
                  Eyke H{\"{u}}llermeier},
  title        = {{Agnostic Explanation of Model Change based on Feature Importance}},
  journal      = {K{\"{u}}nstliche Intelligenz},
  volume       = {36},
  number       = {3},
  pages        = {211--224},
  year         = {2022},
  doi          = {10.1007/S13218-022-00766-6},
keywords = {FF},
}
@inproceedings{
Muschalik.2025,
title={{Exact Computation of Any-Order Shapley Interactions for Graph Neural Networks}},
author={Maximilian Muschalik and Fabian Fumagalli and Paolo Frazzetto and Janine Strotherm and Luca Hermes and Alessandro Sperduti and Eyke H{\"u}llermeier and Barbara Hammer},
booktitle={Proceedings of the Conference on Learning Representations {(ICLR)}},
year={2025},
keywords = {FF},
}
@misc{Wever.2025,
      title={{HyperSHAP: Shapley Values and Interactions for Hyperparameter Importance}}, 
      author={Marcel Wever and Maximilian Muschalik and Fabian Fumagalli and Marius Lindauer},
      year={2025},
      eprint={2502.01276},
      primaryClass={cs.LG},
      keywords = {FF, review}
}
@article{Arrieta.2020,
  author       = {Alejandro Barredo Arrieta and
                  Natalia D{\'{\i}}az Rodr{\'{\i}}guez and
                  Javier Del Ser and
                  Adrien Bennetot and
                  Siham Tabik and
                  Alberto Barbado and
                  Salvador Garc{\'{\i}}a and
                  Sergio Gil{-}Lopez and
                  Daniel Molina and
                  Richard Benjamins and
                  Raja Chatila and
                  Francisco Herrera},
  title        = {{Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI}},
  journal      = {Information Fusion},
  volume       = {58},
  pages        = {82--115},
  year         = {2020},
  doi          = {10.1016/J.INFFUS.2019.12.012},
}
@inproceedings{Zeiler.2014,
	title        = {{Visualizing and Understanding Convolutional Networks}},
	author       = {Zeiler, Matthew D. and Fergus, Rob},
	year         = 2014,
	booktitle    = {Computer Vision - ECCV 2014 - 13th European Conference},
	publisher    = {Springer},
	series       = {Lecture Notes in Computer Science},
	volume       = 8689,
	pages        = {818–833},
	doi          = {10.1007/978-3-319-10590-1_53},
	collection   = {Lecture Notes in Computer Science}
}
@inproceedings{Zintgraf.2017,
  author       = {Luisa M. Zintgraf and
                  Taco S. Cohen and
                  Tameem Adel and
                  Max Welling},
  title        = {{Visualizing Deep Neural Network Decisions: Prediction Difference Analysis}},
  booktitle    = {Proceedings of the International Conference on Learning Representations ({ICLR})},
  year         = {2017},
}
@article{Robnik-Sikonja.2008,
  author       = {Marko Robnik{-}Sikonja and
                  Igor Kononenko},
  title        = {{Explaining Classifications For Individual Instances}},
  journal      = {{IEEE} Transactions on Knowledge and Data Engineering},
  volume       = {20},
  number       = {5},
  pages        = {589--600},
  year         = {2008},
  doi          = {10.1109/TKDE.2007.190734},
}
@inproceedings{Ancona.2018,
  author       = {Marco Ancona and
                  Enea Ceolini and
                  Cengiz {\"{O}}ztireli and
                  Markus Gross},
  title        = {{Towards better understanding of gradient-based attribution methods
                  for Deep Neural Networks}},
  booktitle    = {Proceedings of the International Conference on Learning Representations ({ICLR})},
  year         = {2018},
}
@inproceedings{Shrikumar.2017,
	title        = {{Learning Important Features Through Propagating Activation Differences}},
	author       = {Shrikumar, Avanti and Greenside, Peyton and Kundaje, Anshul},
	year         = 2017,
	booktitle    = {Proceedings of the International Conference on Machine Learning {(ICML)}},
	pages        = {3145–3153},
}
@inproceedings{Koenen.2024,
  author       = {Niklas Koenen and
                  Marvin N. Wright},
  title        = {{Toward Understanding the Disagreement Problem in Neural Network Feature
                  Attribution}},
  booktitle    = {Proceeedings of World Conference on Explainable Artificial Intelligence {(xAI)}},
  pages        = {247--269},
  year         = {2024},
  doi          = {10.1007/978-3-031-63800-8\_13},
}
@article{Smilkov.2017,
	title        = {{SmoothGrad: removing noise by adding noise}},
	author       = {Daniel Smilkov and Nikhil Thorat and Been Kim and Fernanda B. Vi{\'{e}}gas and Martin Wattenberg},
	year         = 2017,
	journal      = {CoRR},
	volume       = {abs/1706.03825},
	eprinttype   = {arXiv},
	eprint       = {1706.03825}
}
@inproceedings{Sundararajan.2017,
	title        = {{Axiomatic Attribution for Deep Networks}},
	author       = {Mukund Sundararajan and Ankur Taly and Qiqi Yan},
	year         = 2017,
	booktitle    = {Proceedings of the International Conference on Machine Learning {(ICML)}},
	pages        = {3319--3328}
}
@article{Bach.2015,
	title        = {{On pixel-wise explanations for non-linear classifier decisions by layer-wise relevance propagation}},
	author       = {Bach, Sebastian and Binder, Alexander and Montavon, Gr{\'e}goire and Klauschen, Frederick and M{\"u}ller, Klaus-Robert and Samek, Wojciech},
	year         = 2015,
	journal      = {PloS one},
	publisher    = {Public Library of Science San Francisco, CA USA},
	volume       = 10,
	number       = 7,
	pages        = {e0130140}
}
@article{Sturmfels.2020,
	title        = {{Visualizing the Impact of Feature Attribution Baselines}},
	author       = {Sturmfels, Pascal and Lundberg, Scott and Lee, Su-In},
	year         = 2020,
	journal      = {Distill},
	doi          = {10.23915/distill.00022},
}
@article{Erion.2021,
	title        = {{Improving performance of deep learning models with axiomatic attribution priors and expected gradients}},
	author       = {Gabriel G. Erion and Joseph D. Janizek and Pascal Sturmfels and Scott M. Lundberg and Su{-}In Lee},
	year         = 2021,
	journal      = {Nature Machine Intelligence},
	volume       = 3,
	number       = 7,
	pages        = {620--631},
	doi          = {10.1038/S42256-021-00343-W}
}
@article{Owen.2013,
	title        = {{Variance Components and Generalized Sobol’ Indices}},
	author       = {Owen, Art B.},
	year         = 2013,
	journal      = {SIAM/ASA Journal on Uncertainty Quantification},
	volume       = 1,
	number       = 1,
	pages        = {19–41},
	doi          = {10.1137/120876782}
}
@article{Sobol.2001,
	title        = {{Global sensitivity indices for nonlinear mathematical models and their Monte Carlo estimates}},
	author       = {Sobol', I. M.},
	year         = 2001,
	journal      = {Mathematics and Computers in Simulation},
	volume       = 55,
	number       = 1,
	pages        = {271–280},
	doi          = {10.1016/S0378-4754(00)00270-6},
	issn         = {0378-4754}
}
@article{Hoeffding.1948,
	title        = {{A Class of Statistics with Asymptotically Normal Distribution}},
	author       = {Hoeffding, Wassily},
	year         = 1948,
	journal      = {The Annals of Mathematical Statistics},
	publisher    = {Institute of Mathematical Statistics},
	volume       = 19,
	number       = 3,
	pages        = {293–325},
	doi          = {10.1214/aoms/1177730196}
}
@article{Stone.1994,
	title        = {{The Use of Polynomial Splines and Their Tensor Products in Multivariate Function Estimation}},
	author       = {Charles J. Stone},
	year         = 1994,
	journal      = {The Annals of Statistics},
	publisher    = {Institute of Mathematical Statistics},
	volume       = 22,
	number       = 1,
	pages        = {118 -- 171},
}
@article{Chastaing.2012,
	title        = {{Generalized Hoeffding-Sobol decomposition for dependent variables - application to sensitivity analysis}},
	author       = {Chastaing, Gaelle and Gamboa, Fabrice and Prieur, Clémentine},
	year         = 2012,
	journal      = {Electronic Journal of Statistics},
	publisher    = {Institute of Mathematical Statistics and Bernoulli Society},
	volume       = 6,
	pages        = {2420–2448},
	doi          = {10.1214/12-EJS749}
}
@article{Strobl.2008,
	title        = {{Conditional variable importance for random forests}},
	author       = {Strobl, Carolin and Boulesteix, Anne-Laure and Kneib, Thomas and Augustin, Thomas and Zeileis, Achim},
	year         = 2008,
	journal      = {BMC bioinformatics},
	publisher    = {Springer},
	volume       = 9,
	pages        = {1--11},
    doi          = {10.1186/1471-2105-9-307},
}
@article{Apley.2020,
	title        = {{Visualizing the effects of predictor variables in black box supervised learning models}},
	author       = {Apley, Daniel W. and Zhu, Jingyu},
	year         = 2020,
	journal      = {Journal of the Royal Statistical Society: Series B (Statistical Methodology)},
	volume       = 82,
    doi = {10.1111/rssb.12377},
	number       = 4,
	pages        = {1059--1086},
}
@article{Friedman.2008,
	title        = {{Predictive Learning via Rule Ensembles}},
	author       = {Friedman, Jerome H. and Popescu, Bogdan E.},
	year         = 2008,
	journal      = {The Annals of Applied Statistics},
	volume       = 2,
	number       = 3,
	pages        = {916--954},
}
@article{Strumbelj.2010,
	title        = {{An Efficient Explanation of Individual Classifications using Game Theory}},
	author       = {Erik Strumbelj and Igor Kononenko},
	year         = 2010,
	journal      = {Journal of Machine Learning Research},
	volume       = 11,
	pages        = {1--18},
	doi          = {10.5555/1756006.1756007}
}
@inproceedings{Chau.2022,
  author       = {Siu Lun Chau and
                  Robert Hu and
                  Javier Gonz{\'{a}}lez and
                  Dino Sejdinovic},
  editor       = {Sanmi Koyejo and
                  S. Mohamed and
                  A. Agarwal and
                  Danielle Belgrave and
                  K. Cho and
                  A. Oh},
  title        = {{RKHS-SHAP:} Shapley Values for Kernel Methods},
  booktitle    = {Proceedings of Advances in Neural Information Processing Systems {(NeurIPS)}},
  year         = {2022},
}
@inproceedings{Chau.2023,
  author       = {Siu Lun Chau and
                  Krikamol Muandet and
                  Dino Sejdinovic},
  editor       = {Alice Oh and
                  Tristan Naumann and
                  Amir Globerson and
                  Kate Saenko and
                  Moritz Hardt and
                  Sergey Levine},
  title        = {Explaining the Uncertain: Stochastic Shapley Values for Gaussian Process
                  Models},
  booktitle    = {Proceedings of Advances in Neural Information Processing Systems {(NeurIPS)}},
  year         = {2023},
}
@book{Robert.1999,
  title={Monte Carlo statistical methods},
  author={Robert, Christian P and Casella, George and Casella, George},
  volume={2},
  year={1999},
  publisher={Springer},
  doi={10.1007/978-1-4757-4145-2},
}
@article{Mitchell.2022,
	title        = {{Sampling Permutations for Shapley Value Estimation}},
	author       = {Rory Mitchell and Joshua Cooper and Eibe Frank and Geoffrey Holmes},
	year         = 2022,
	journal      = {Journal of Machine Learning Research},
	volume       = 23,
    number={43},
    pages={1--46},
}
@misc{Olsen.2024b,
    author       = {Lars Henry Berge Olsen and
                  Martin Jullum},
    title        = {Improving the Sampling Strategy in KernelSHAP},
    journal      = {CoRR},
    volume       = {abs/2410.04883},
    year         = {2024},
    eprinttype    = {arXiv},
    eprint       = {2410.04883},
}
@inproceedings{Baniecki.2025,
    title={{Efficient and Accurate Explanation Estimation with Distribution Compression}},
    author={Hubert Baniecki and Giuseppe Casalicchio and Bernd Bischl and Przemyslaw Biecek},
    booktitle={{Proccedings of the International Conference on Learning Representations (ICLR)}},
    year={2025},
}
@inproceedings{Lundstrom.2023,
	title        = {{A unifying framework to the analysis of interaction methods using synergy functions}},
	author       = {Lundstrom, Daniel and Razaviyayn, Meisam},
	year         = 2023,
	booktitle    = {Proceedings of the International Conference on Machine Learning {(ICML)}},
	pages        = {23005--23032},
}
@inproceedings{Han.2022,
  author       = {Tessa Han and
                  Suraj Srinivas and
                  Himabindu Lakkaraju},
  editor       = {Sanmi Koyejo and
                  S. Mohamed and
                  A. Agarwal and
                  Danielle Belgrave and
                  K. Cho and
                  A. Oh},
  title        = {Which Explanation Should {I} Choose? {A} Function Approximation Perspective
                  to Characterizing Post Hoc Explanations},
  booktitle    = {Proceedings of Advances in Neural Information Processing Systems 35 {(NeurIPS)}},
  year         = {2022},
}
@inproceedings{Duval.2021,
	title        = {{GraphSVX: Shapley Value Explanations for Graph Neural Networks}},
	author       = {Alexandre Duval and Fragkiskos D. Malliaros},
	year         = 2021,
	booktitle    = {Machine Learning and Knowledge Discovery in Databases. Research Track - European Conference ({ECML} {PKDD})},
	volume       = 12976,
	pages        = {302--318},
    doi          = {10.1007/978-3-030-86520-7\_19},
}
@inproceedings{Ye.2023,
	title        = {{SAME}: Uncovering {GNN} Black Box with Structure-aware Shapley-based Multipiece Explanations},
	author       = {Ziyuan Ye and Rihan Huang and Qilin Wu and Quanying Liu},
	year         = 2023,
	booktitle    = {Proceedings of Advances in Neural Information Processing Systems {(NeurIPS)}},
}
@inproceedings{Yuan.2021,
	title        = {On Explainability of Graph Neural Networks via Subgraph Explorations},
	author       = {Hao Yuan and Haiyang Yu and Jie Wang and Kang Li and Shuiwang Ji},
	year         = 2021,
	booktitle    = {Proceedings of the International Conference on Machine Learning ({ICML})},
	volume       = 139,
	pages        = {12241--12252}
}
@inproceedings{Bui.2024,
	title        = {Explaining Graph Neural Networks via Structure-aware Interaction Index},
	author       = {Ngoc Bui and Hieu Trung Nguyen and Viet Anh Nguyen and Rex Ying},
	year         = 2024,
	booktitle    = {Proceedings of the International Conference on Machine Learning ({ICML})}
}
@article{Bahri.2021,
	title        = {{Data stream analysis: Foundations, major tasks and tools}},
	author       = {Bahri, Maroua and Bifet, Albert and Gama, Jo{a}o and Gomes, Heitor Murilo and Maniu, Silviu},
	year         = 2021,
	journal      = {Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery},
	volume       = 11,
	number       = 3,
	pages        = {e1405},
	doi       = {10.1002/widm.1405}
}
@inproceedings{Ta.2016,
	title        = {{Big data stream computing in healthcare real-time analytics}},
	author       = {Van-Dai Ta and Chuan-Ming Liu and Nkabinde, Goodwill Wandile},
	year         = 2016,
	booktitle    = {{Proceddings of the International Conference on Cloud Computing and Big Data Analysis (ICCCBDA)}},
	pages        = {37–42},
	doi          = {10.1109/ICCCBDA.2016.7529531}
}
@inproceedings{Davari.2021,
	title        = {{Predictive maintenance based on anomaly detection using deep learning for air production unit in the railway industry}},
	author       = {Narjes Davari and Bruno Veloso and Rita P. Ribeiro and Pedro Mota Pereira and Jo{\~{a}}o Gama},
	year         = 2021,
	booktitle    = {{Proceedings of the IEEE International Conference on Data Science and Advanced Analytics (DSAA)}},
	pages        = {1--10},
	doi          = {10.1109/DSAA53316.2021.9564181}
}
@inproceedings{Koh.2021,
  author       = {Pang Wei Koh and
                  Shiori Sagawa and
                  Henrik Marklund and
                  Sang Michael Xie and
                  Marvin Zhang and
                  Akshay Balsubramani and
                  Weihua Hu and
                  Michihiro Yasunaga and
                  Richard Lanas Phillips and
                  Irena Gao and
                  Tony Lee and
                  Etienne David and
                  Ian Stavness and
                  Wei Guo and
                  Berton Earnshaw and
                  Imran S. Haque and
                  Sara M. Beery and
                  Jure Leskovec and
                  Anshul Kundaje and
                  Emma Pierson and
                  Sergey Levine and
                  Chelsea Finn and
                  Percy Liang},
  editor       = {Marina Meila and
                  Tong Zhang},
  title        = {{WILDS:} {A} Benchmark of in-the-Wild Distribution Shifts},
  booktitle    = {Proceedings of the International Conference on Machine Learning {(ICLM})},
  pages        = {5637--5664},
  year         = {2021},
}
@article{Losing.2018,
	title        = {{Incremental On-line Learning: A Review and Comparison of State of the Art Algorithms}},
	shorttitle   = {Incremental on-line learning},
	author       = {Losing, Viktor and Hammer, Barbara and Wersing, Heiko},
	year         = 2018,
	journal      = {Neurocomputing},
	volume       = 275,
	pages        = {1261--1274},
	doi       = {10.1016/j.neucom.2017.06.084}
}
@article{Gama.2014,
	title        = {{A Survey on Concept Drift Adaptation}},
	author       = {Jo{\~{a}}o Gama and Indre Zliobaite and Albert Bifet and Mykola Pechenizkiy and Abdelhamid Bouchachia},
	year         = 2014,
	journal      = {{ACM} Computing Surveys},
	volume       = 46,
	number       = 4,
	pages        = {44:1--44:37},
	doi          = {10.1145/2523813}
}
@article{Vitter.1985,
	title        = {{Random Sampling with a Reservoir}},
	author       = {Vitter, Jeffrey S.},
	year         = 1985,
	journal      = {ACM Transactions on Mathematical Software},
	volume       = 11,
	number       = 1,
	pages        = {37–57},
	  doi       = {10.1016/j.ipl.2005.11.003}
}
@inproceedings{Herbinger.2022,
  author       = {Julia Herbinger and
                  Bernd Bischl and
                  Giuseppe Casalicchio},
  editor       = {Gustau Camps{-}Valls and
                  Francisco J. R. Ruiz and
                  Isabel Valera},
  title        = {{REPID:} Regional Effect Plots with implicit Interaction Detection},
  booktitle    = {Proceedings of the International Conference on Artificial Intelligence and Statistics ({AISTATS})},
  pages        = {10209--10233},
  year         = {2022},
}
@inproceedings{Laberge.2024,
  author       = {Gabriel Laberge and
                  Yann Batiste Pequignot and
                  Mario Marchand and
                  Foutse Khomh},
  editor       = {Sanjoy Dasgupta and
                  Stephan Mandt and
                  Yingzhen Li},
  title        = {Tackling the {XAI} Disagreement Problem with Regional Explanations},
  booktitle    = {Proceedings of the International Conference on Artificial Intelligence and Statistics ({AISTATS})},
  pages        = {2017--2025},
  year         = {2024},
}
@inproceedings{Kang.2024,
  author       = {Justin Singh Kang and
                  Yigit Efe Erginbas and
                  Landon Butler and
                  Ramtin Pedarsani and
                  Kannan Ramchandran},
  title        = {Learning to Understand: Identifying Interactions via the M{\"{o}}bius Transform},
  booktitle    = {Proceedings of Advances in Neural Information Processing Systems ({NeurIPS})},
  year         = {2024},
}
@inproceedings{Wang.2023,
  author       = {Jiachen T. Wang and
                  Ruoxi Jia},
  editor       = {Francisco J. R. Ruiz and
                  Jennifer G. Dy and
                  Jan{-}Willem van de Meent},
  title        = {Data Banzhaf: {A} Robust Data Valuation Framework for Machine Learning},
  booktitle    = {Proceedings of the International Conference on Artificial Intelligence and Statistics ({AISTATS})},
  pages        = {6388--6421},
  year         = {2023},
}
@article{Krishna.2024,
  author       = {Satyapriya Krishna and
                  Tessa Han and
                  Alex Gu and
                  Steven Wu and
                  Shahin Jabbari and
                  Himabindu Lakkaraju},
  title        = {The Disagreement Problem in Explainable Machine Learning: {A} Practitioner's
                  Perspective},
  journal      = {Transactions on Machine Learning Research},
  volume       = {2024},
  year         = {2024},
}
@article{Lu.2019,
  author       = {Jie Lu and
                  Anjin Liu and
                  Fan Dong and
                  Feng Gu and
                  Jo{\~{a}}o Gama and
                  Guangquan Zhang},
  title        = {Learning under Concept Drift: {A} Review},
  journal      = {{IEEE} Transactions on Knowledge and Data Engineering},
  volume       = {31},
  number       = {12},
  pages        = {2346--2363},
  year         = {2019},
  doi          = {10.1109/TKDE.2018.2876857},
}
@inproceedings{Ribeiro.2016,
  author       = {Marco T{\'{u}}lio Ribeiro and
                  Sameer Singh and
                  Carlos Guestrin},
  title        = {"Why Should {I} Trust You?": Explaining the Predictions of Any Classifier},
  booktitle    = {Proceedings of the {ACM} {SIGKDD} International Conference on
                  Knowledge Discovery and Data Mining},
  pages        = {1135--1144},
  year         = {2016},
  doi          = {10.1145/2939672.2939778},
}
@article{Gunning.2019,
  author       = {David Gunning and
                  Mark Stefik and
                  Jaesik Choi and
                  Timothy Miller and
                  Simone Stumpf and
                  Guang{-}Zhong Yang},
  title        = {{XAI} - Explainable artificial intelligence},
  journal      = {Science Robotics},
  volume       = {4},
  number       = {37},
  year         = {2019},
  doi          = {10.1126/SCIROBOTICS.AAY7120},
}
@article{Blesch.2024, 
    title={Conditional feature importance for mixed data}, 
     volume={108},
     DOI={10.1007/s10182-023-00477-9}, 
     number={2}, 
     journal={AStA Advances in Statistical Analysis}, author={Blesch, Kristin and Watson, David S. and Wright, Marvin N.}, year={2024}, pages={259–278} 
}
@article{Blucher.2022,
  author       = {Stefan Bl{\"{u}}cher and
                  Johanna Vielhaben and
                  Nils Strodthoff},
  title        = {PredDiff: Explanations and interactions from conditional expectations},
  journal      = {Artificial Intelligence},
  volume       = {312},
  pages        = {103774},
  year         = {2022},
  doi          = {10.1016/J.ARTINT.2022.103774},
}
@article{Kuo.2010,
  author       = {Frances Y. Kuo and
                  Ian H. Sloan and
                  Grzegorz W. Wasilkowski and
                  Henryk Wozniakowski},
  title        = {On decompositions of multivariate functions},
  journal      = {Mathematical Computing},
  volume       = {79},
  number       = {270},
  pages        = {953--966},
  year         = {2010},
  doi          = {10.1090/S0025-5718-09-02319-9},
}
@article{Hama.2023,
  author       = {Naofumi Hama and
                  Masayoshi Mase and
                  Art B. Owen},
  title        = {Deletion and Insertion Tests in Regression Models},
  journal      = {Journal of Machine Learning Research},
  volume       = {24},
  pages        = {290:1--290:38},
  year         = {2023},
}
@inproceedings{Mohammadi.2025,
  author       = {Majid Mohammadi and
                  Ilaria Tiddi and
                  Annette ten Teije},
  title        = {Unlocking the Game: Estimating Games in M{\"{o}}bius Representation
                  for Explanation and High-Order Interaction Detection},
	booktitle    = {Proceeedings of the {AAAI} Conference on Artificial Intelligence {(AAAI)}},
  pages        = {19512--19519},
  year         = {2025},
  doi          = {10.1609/AAAI.V39I18.34148},
}



@article{Pelegrina.2025,
  author       = {Guilherme Dean Pelegrina and
                  Patrick Kolpaczki and
                  Eyke H{\"{u}}llermeier},
  title        = {Shapley Value Approximation Based on k-Additive Games},
  journal      = {CoRR},
  volume       = {abs/2502.04763},
  year         = {2025},
  eprinttype    = {arXiv},
  eprint       = {2502.04763},
}
@article{Weber.2023,
  author       = {Leander Weber and
                  Sebastian Lapuschkin and
                  Alexander Binder and
                  Wojciech Samek},
  title        = {Beyond explaining: Opportunities and challenges of XAI-based model
                  improvement},
  journal      = {Information Fusion},
  volume       = {92},
  pages        = {154--176},
  year         = {2023},
  doi          = {10.1016/J.INFFUS.2022.11.013},
}
@article{Lapuschkin.2019,
  title={Unmasking Clever Hans predictors and assessing what machines really learn},
  author={Lapuschkin, Sebastian and W{\"a}ldchen, Stephan and Binder, Alexander and Montavon, Gr{\'e}goire and Samek, Wojciech and M{\"u}ller, Klaus-Robert},
  journal={Nature communications},
  volume={10},
  number={1},
  pages={1096},
  year={2019},
  doi={10.1038/s41467-019-08987-4}
}
@article{Saarela.2024,
  title={Recent applications of Explainable AI (XAI): A systematic literature review},
  author={Saarela, Mirka and Podgorelec, Vili},
  journal={Applied Sciences},
  volume={14},
  number={19},
  pages={8884},
  year={2024},
  doi={10.3390/app14198884}
}
@article{Mindlin.2025,
  author       = {Dimitry Mindlin and
                  Fabian Beer and
                  Leonie Nora Sieger and
                  Stefan Heindorf and
                  Elena Esposito and
                  Axel{-}Cyrille Ngonga Ngomo and
                  Philipp Cimiano},
  title        = {Beyond one-shot explanations: a systematic literature review of dialogue-based
                  xAI approaches},
  journal      = {Artificial Intelligence Review},
  volume       = {58},
  number       = {3},
  pages        = {81},
  year         = {2025},
  doi          = {10.1007/S10462-024-11007-7},
}
@article{Roscher.2020,
  author       = {Ribana Roscher and
                  Bastian Bohn and
                  Marco F. Duarte and
                  Jochen Garcke},
  title        = {Explainable Machine Learning for Scientific Insights and Discoveries},
  journal      = {{IEEE} Access},
  volume       = {8},
  pages        = {42200--42216},
  year         = {2020},
  doi          = {10.1109/ACCESS.2020.2976199},
}
@article{Liu.2023,
  title={The emergent role of explainable artificial intelligence in the materials sciences},
  author={Liu, Tommy and Barnard, Amanda S},
  journal={Cell Reports Physical Science},
  volume={4},
  number={10},
  year={2023},
  doi={10.1016/j.xcrp.2023.101630}
}
@inproceedings{Ewald.2024,
	title        = {{A Guide to Feature Importance Methods for Scientific Inference}},
	author       = {Ewald, Fiona Katharina and Bothmann, Ludwig and Wright, Marvin N. and Bischl, Bernd and Casalicchio, Giuseppe and K{\"o}nig, Gunnar},
	year         = 2024,
	booktitle    = {Proceeedings of World Conference on Explainable Artificial Intelligence {(xAI)}},
	pages        = {440--464}
}
@inproceedings{Molnar.2022,
	title        = {{General pitfalls of model-agnostic interpretation methods for machine learning models}},
	author       = {Molnar, Christoph and K{\"o}nig, Gunnar and Herbinger, Julia and Freiesleben, Timo and Dandl, Susanne and Scholbeck, Christian A and Casalicchio, Giuseppe and Grosse-Wentrup, Moritz and Bischl, Bernd},
	year         = 2022,
	booktitle    = {xxAI-Beyond Explainable AI: International Workshop, Held in Conjunction with ICML 2020},
	pages        = {39--68},
}
@article{Tan.2023,
  title={Considerations when learning additive explanations for black-box models},
  author={Tan, Sarah and Hooker, Giles and Koch, Paul and Gordo, Albert and Caruana, Rich},
  journal={Machine Learning},
  volume={112},
  number={9},
  pages={3333--3359},
  year={2023},
  doi={10.1007/s10994-023-06335-8}
}
@article{Wachter.2017,
  title={{Counterfactual explanations without opening the black box: Automated decisions and the GDPR}},
  author={Wachter, Sandra and Mittelstadt, Brent and Russell, Chris},
  journal={Harvard Journal of Law & Technology},
  volume={31},
  pages={841},
  year={2017}
}
@inproceedings{Radford.2021,
  author       = {Alec Radford and
                  Jong Wook Kim and
                  Chris Hallacy and
                  Aditya Ramesh and
                  Gabriel Goh and
                  Sandhini Agarwal and
                  Girish Sastry and
                  Amanda Askell and
                  Pamela Mishkin and
                  Jack Clark and
                  Gretchen Krueger and
                  Ilya Sutskever},
  title        = {Learning Transferable Visual Models From Natural Language Supervision},
  booktitle    = {Proceedings of the International Conference on Machine Learning {ICML}},
  pages        = {8748--8763},
  year         = {2021},
}
@article{Baniecki.2025b,
  title={{Explaining Similarity in Vision-Language Encoders with Weighted Banzhaf Interactions}},
  author={Baniecki, Hubert and Muschalik, Maximilian and Fumagalli, Fabian and Hammer, Barbara and H{\"u}llermeier, Eyke and Biecek, Przemyslaw},
  journal = {CoRR},
  volume = {abs/2508.05430},
  eprinttype = {arXiv},
  eprint = {2508.05430},
  year={2025}
}
@book{Glasserman.2004,
  title={Monte Carlo Methods in Financial Engineering},
  author={Glasserman, Paul},
  year={2004},
  publisher={Springer},
  series={Stochastic Modelling and Applied Probability},
  doi={10.1007/978-0-387-21617-1}
}
@article{Mayer.2025,
  title={Shapley Values: Paired-Sampling Approximations},
  author={Mayer, Michael and W{\"u}thrich, Mario V},
  journal = {CoRR},
  volume = {abs/2508.12947},
  eprinttype = {arXiv},
  eprint = {2508.12947},
  year = {2025},
}


@inproceedings{Kelodjou.2024,
  author       = {Gwladys Kelodjou and
                  Laurence Roz{\'{e}} and
                  V{\'{e}}ronique Masson and
                  Luis Gal{\'{a}}rraga and
                  Romaric Gaudel and
                  Maurice Tchuent{\'{e}} and
                  Alexandre Termier},
  editor       = {Michael J. Wooldridge and
                  Jennifer G. Dy and
                  Sriraam Natarajan},
  title        = {Shaping Up {SHAP:} Enhancing Stability through Layer-Wise Neighbor
                  Selection},
  booktitle    = {Proceedings of the {AAAI} Conference on Artificial Intelligence ({AAAI})},
  pages        = {13094--13103},
  year         = {2024},
  doi          = {10.1609/AAAI.V38I12.29208},
}
@article{Hoeffding.1963,
  title={Probability inequalities for sums of bounded random variables},
  author={Hoeffding, Wassily},
  journal={Journal of the American statistical association},
  volume={58},
  number={301},
  pages={13--30},
  year={1963},
}
@article{Witter.2025,
  author       = {R. Teal Witter and
                  Yurong Liu and
                  Christopher Musco},
  title        = {Regression-adjusted Monte Carlo Estimators for Shapley Values and
                  Probabilistic Values},
  journal      = {CoRR},
  volume       = {abs/2506.11849},
  year         = {2025},
  eprinttype    = {arXiv},
  eprint       = {2506.11849},
}

% Teal added
@InProceedings{Kwon.2022b,
  title = 	 { Beta Shapley: a Unified and Noise-reduced Data Valuation Framework for Machine Learning },
  author =       {Kwon, Yongchan and Zou, James},
  booktitle = 	 {{Proceedings of the International Conference on Artificial Intelligence and Statistics {(AISTATS)}}},
  pages = 	 {8780--8802},
  year = 	 {2022},
}

@book{Molnar.2024,
	author = {Molnar, Christoph},
	publisher = {Leanpub},
	title = {Interpretable machine learning: A guide for making black box models explainable},
	year = {2024}}

@article{Cortez.2007,
  title={A data mining approach to predict forest fires using meteorological data},
  author={Cortez, Paulo and Morais, An{\'\i}bal de Jesus Raimundo},
  year={2007},
  pubulisher={Associa{\c{c}}{\~a}o Portuguesa para a Intelig{\^e}ncia Artificial (APPIA)}
}

@article{Yeh.2018b,
  author       = {I{-}Cheng Yeh and
                  Tzu{-}Kuang Hsu},
  title        = {Building real estate valuation models with comparative approach through
                  case-based reasoning},
  journal      = {Appied Soft Computing},
  volume       = {65},
  pages        = {260--271},
  year         = {2018},
  doi          = {10.1016/J.ASOC.2018.01.029},
}
@inproceedings{Street.1993,
  title={Nuclear feature extraction for breast tumor diagnosis},
  author={Street, W Nick and Wolberg, William H and Mangasarian, Olvi L},
  booktitle={Biomedical image processing and biomedical visualization},
  volume={1905},
  pages={861--870},
  year={1993},
}
@article{Dinh.2019,
  author       = {An Dinh and
                  Stacey Miertschin and
                  Amber Young and
                  Somya D. Mohanty},
  title        = {A data-driven approach to predicting diabetes and cardiovascular disease
                  with machine learning},
  journal      = {{BMC} Medical Informatics Decisision Making},
  volume       = {19},
  number       = {1},
  pages        = {211:1--211:15},
  year         = {2019},
  doi          = {10.1186/S12911-019-0918-5},
}
@misc{Redmond.2011,
  author       = {Michael Redmond},
  title        = {Communities and Crime Unnormalized},
  publisher    = {{UCI} Machine Learning Repository},
  year         = {2011},
  doi          = {10.24432/C5PC8X},
}

@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}
}
@inproceedings{Dosovitskiy.2021,
  author       = {Alexey Dosovitskiy and
                  Lucas Beyer and
                  Alexander Kolesnikov and
                  Dirk Weissenborn and
                  Xiaohua Zhai and
                  Thomas Unterthiner and
                  Mostafa Dehghani and
                  Matthias Minderer and
                  Georg Heigold and
                  Sylvain Gelly and
                  Jakob Uszkoreit and
                  Neil Houlsby},
  title        = {An Image is Worth 16x16 Words: Transformers for Image Recognition
                  at Scale},
  booktitle    = {Proceedings of the International Conference on Learning Representations ({ICLR})},
  year         = {2021},
}
@article{Krizhevsky.2009,
  title={Learning multiple layers of features from tiny images},
  author={Krizhevsky, Alex and Hinton, Geoffrey and others},
  year={2009},
  journal={Technical Report},
}

@inproceedings{lam2022greedy,
  title={Greedy relaxations of the sparsest permutation algorithm},
  author={Lam, Wai-Yin and Andrews, Bryan and Ramsey, Joseph},
  booktitle={Uncertainty in Artificial Intelligence},
  pages={1052--1062},
  year={2022},
  organization={PMLR}
}
@inproceedings{ghorbani2019data,
  title={Data shapley: Equitable valuation of data for machine learning},
  author={Ghorbani, Amirata and Zou, James},
  booktitle={International conference on machine learning},
  pages={2242--2251},
  year={2019},
  organization={PMLR}
}
@inproceedings{musco2025provably,
  author       = {Christopher Musco and
                  R. Teal Witter},
  title        = {{Provably Accurate Shapley Value Estimation via Leverage Score Sampling}},
  booktitle = {Proccedings of the International Conference on Learning Representations (ICLR)},
  year         = {2025},
}
@inproceedings{butler2025proxyspex,
title={Proxy-{SPEX}: Sample-Efficient Interpretability via Sparse Feature Interactions in {LLM}s},
author={Landon Butler and Abhineet Agarwal and Justin Singh Kang and Yigit Efe Erginbas and Bin Yu and Kannan Ramchandran},
booktitle={The Thirty-ninth Annual Conference on Neural Information Processing Systems},
year={2025},
url={https://openreview.net/forum?id=KI8qan2EA7}
}
@inproceedings{kang2025spex,
title={{SPEX}: Scaling Feature Interaction Explanations for {LLM}s},
author={Justin Singh Kang and Landon Butler and Abhineet Agarwal and Yigit Efe Erginbas and Ramtin Pedarsani and Bin Yu and Kannan Ramchandran},
booktitle={ICLR 2025 Workshop on Building Trust in Language Models and Applications},
year={2025},
url={https://openreview.net/forum?id=lNCwWLMCwO}
}
@inproceedings{fumagalli2024kernelshapiq,
	title        = {{KernelSHAP-IQ: Weighted Least Square Optimization for Shapley Interactions}},
	author       = {Fabian Fumagalli and Maximilian Muschalik and Patrick Kolpaczki and Eyke H{\"{u}}llermeier and Barbara Hammer},
	year         = 2024,
	booktitle    = {Proceedings of the International Conference on Machine Learning ({ICML})},
    pages =  {14308--14342},
    keywords = {FF},
}
@article{tsai2022faith,
	title        = {{Faith-Shap: The Faithful Shapley Interaction Index}},
	author       = {Che{-}Ping Tsai and Chih{-}Kuan Yeh and Pradeep Ravikumar},
	year         = 2023,
	journal      = {Journal of Machine Learning Research},
	volume       = 24,
	number       = 94,
	pages        = {1--42}
}

@article{holland1986statistics,
  title={Statistics and causal inference},
  author={Holland, Paul W},
  journal={Journal of the American statistical Association},
  volume={81},
  number={396},
  pages={945--960},
  year={1986},
  publisher={Taylor \& Francis}
}

@article{rubin1974estimating,
  title={Estimating causal effects of treatments in randomized and nonrandomized studies.},
  author={Rubin, Donald B},
  journal={Journal of educational Psychology},
  volume={66},
  number={5},
  pages={688},
  year={1974},
  publisher={American Psychological Association}
}

@book{peters2017elements,
  title={Elements of causal inference: foundations and learning algorithms},
  author={Peters, Jonas and Janzing, Dominik and Sch{\"o}lkopf, Bernhard},
  year={2017},
  publisher={The MIT press}
}

@article{bareinboim2016causal,
  title={Causal inference and the data-fusion problem},
  author={Bareinboim, Elias and Pearl, Judea},
  journal={Proceedings of the National Academy of Sciences},
  volume={113},
  number={27},
  pages={7345--7352},
  year={2016},
  publisher={National Academy of Sciences}
}

@article{li2024robust,
  title={Robust data valuation with weighted banzhaf values},
  author={Li, Weida and Yu, Yaoliang},
  journal={Advances in Neural Information Processing Systems},
  volume={36},
  year={2024}
}

@book{pearl2009causality,
  title={Causality: Models, Reasoning and Inference},
  author={Pearl, Judea},
  year={2009},
  publisher={Cambridge University Press},
  edition="Second"
}

@article{JMLR:v26:25-0512,
  author  = {Si-Yang Liu and
			 Hao-Run Cai and
 			 Qi-Le Zhou and
			 Huai-Hong Yin and
			 Tao Zhou and
			 Jun-Peng Jiang and
			 Han-Jia Ye},
  title   = {Talent: A Tabular Analytics and Learning Toolbox},
  journal = {Journal of Machine Learning Research},
  year    = {2025},
  volume  = {26},
  number  = {226},
  pages   = {1--16},
  url     = {http://jmlr.org/papers/v26/25-0512.html}
}