The explanation game: Explaining machine learning models using shapley values
Open accessThe claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Counterfactual reasoning in computer science is defined as the process of evaluating conditional claims about alternative possibilities and their consequences.
“Thus, counterfactual reasoning in computer science is defined as the process of evaluating conditional claims about alternative possibilities and their consequences [33].”
From A Survey of Contrastive and Counterfactual Explanation Generation Methods for Explainable Artificial Intelligence (2021) · cites this paper as [33]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
A Unified Approach to Interpreting Model Predictions
Lundberg, S., & Lee, S.-I. (2017) · arXiv · doi:10.48550/arxiv.1705.07874 · Cited by 23,707 · see citing works
"Why Should I Trust You?": Explaining the Predictions of Any Classifier
Ribeiro, M. T., Singh, S., & Guestrin, C. (2016) · Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining · doi:10.1145/2939672.2939778 · Cited by 17,555 · see citing works
Gauriat, C.-M., Pencolé, Y., Ribot, P., et al. (2024) · HAL · doi:10.4230/oasics.dx.2024.27 · Cited by 12,623 · see citing works
Proceedings of the 24th international conference on Machine learning
(2007) · doi:10.1145/1273496 · Cited by 11,869 · see citing works
Shapley, L. (1988) · Cambridge University Press eBooks · doi:10.1017/cbo9780511528446.003 · Cited by 10,698 · see citing works
LightGBM: A Highly Efficient Gradient Boosting Decision Tree
(2017) · HAL · Cited by 10,512 · see citing works
Efron, B., & Tibshirani, R. (1986) · Statistical Science · doi:10.1214/ss/1177013815 · Cited by 6,033 · see citing works
Explanation in artificial intelligence: Insights from the social sciences
Miller, T. (2019) · Artificial Intelligence · doi:10.1016/j.artint.2018.07.007 · Cited by 5,302 · see citing works
Axiomatic attribution for deep networks
Sundararajan, M., Taly, A., & Yan, Q. (2017) · arXiv · doi:10.48550/arxiv.1703.01365 · Cited by 4,087 · see citing works
Self-citationNorm theory: Comparing reality to its alternatives
Kahneman, D., & Miller, D. T. (1986) · Psychological Review · doi:10.1037/0033-295x.93.2.136 · Cited by 3,158 · see citing works
Learning Important Features Through Propagating Activation Differences
Shrikumar, A., Greenside, P., & Kundaje, A. (2017) · arXiv · doi:10.48550/arxiv.1704.02685 · Cited by 2,761 · see citing works
Explaining prediction models and individual predictions with feature contributions
Štrumbelj, E., & Kononenko, I. (2014) · Knowledge and Information Systems · doi:10.1007/s10115-013-0679-x · Cited by 2,286 · see citing works
Consistent Individualized Feature Attribution for Tree Ensembles
Lundberg, S. M., Erion, G. G., & Lee, S.-I. (2018) · arXiv · doi:10.48550/arxiv.1802.03888 · Cited by 1,430 · see citing works
Aas, K., Jullum, M., & Løland, A. (2021) · Artificial Intelligence · doi:10.1016/j.artint.2021.103502 · Cited by 1,159 · see citing works
Monotonic solutions of cooperative games
Young, H. P. (1985) · International Journal of Game Theory · doi:10.1007/bf01769885 · Cited by 914 · see citing works
Mittelstadt, B., Russell, C., & Wachter, S. (2019) · Proceedings of the Conference on Fairness, Accountability, and Transparency · doi:10.1145/3287560.3287574 · Cited by 794 · see citing works
Explanations based on the Missing: Towards Contrastive Explanations with Pertinent NegativesPreprint
Dhurandhar, A., Chen, P.-Y., Luss, R., Tu, C.-C., Ting, P.-S., Shanmugam, K., & Das, P. (2018) · Neural Information Processing Systems · doi:10.48550/arxiv.1802.07623 · Cited by 730 · see citing works
Datta, A., Sen, S., & Zick, Y. (2016) · 2016 IEEE Symposium on Security and Privacy (SP) · doi:10.1109/sp.2016.42 · Cited by 728 · see citing works
An Efficient Explanation of Individual Classifications using Game Theory
Štrumbelj, E., & Kononenko, I. (2010) · Repository of the University of Ljubljana (University of Ljubljana) · doi:10.5555/1756006.1756007 · Cited by 570 · see citing works
Data Shapley: Equitable Valuation of Data for Machine LearningPreprint
Ghorbani, A., & Zou, J. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1904.02868 · Cited by 434 · see citing works
The many Shapley values for model explanationPreprint
Sundararajan, M., & Najmi, A. (2019) · RePEc: Research Papers in Economics · doi:10.48550/arxiv.1908.08474 · Cited by 415 · see citing works
Hitchcock, C., & Knobe, J. (2009) · The Journal of Philosophy · doi:10.5840/jphil20091061128 · Cited by 371 · see citing works
Lipton, P. (1990) · Royal Institute of Philosophy Supplement · doi:10.1017/s1358246100005130 · Cited by 343 · see citing works
Gradient-Based Attribution Methods
Ancona, M., Ceolini, E., Öztireli, C., & Groß, M. H. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-28954-6_9 · Cited by 263 · see citing works
Feature relevance quantification in explainable AI: A causal problem
Janzing, D., Minorics, L., & Blöbaum, P. (2019) · arXiv · doi:10.48550/arxiv.1910.13413 · Cited by 207 · see citing works
L-Shapley and C-Shapley: Efficient Model Interpretation for Structured DataPreprint
Chen, J., Song, L., Wainwright, M. J., & Jordan, M. I. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1808.02610 · Cited by 108 · see citing works
Feature selection based on the Shapley value
Cohen, S. B., Ruppin, E., & Dror, G. (2005) · Edinburgh Research Explorer (University of Edinburgh) · Cited by 103 · see citing works
Explaining Deep Neural Networks with a Polynomial Time Algorithm for Shapley Values Approximation
Ancona, M., Öztireli, C., & Groß, M. H. (2019) · Repository for Publications and Research Data (ETH Zurich) · doi:10.3929/ethz-b-000342737 · Cited by 94 · see citing works
The problem of causal selection.
Hesslow, G. (1988) · Cited by 86 · see citing works
Bounding the Estimation Error of Sampling-based Shapley Value ApproximationPreprint
Maleki, S., Tran-Thanh, L., Hines, G., Rahwan, T., & Rogers, A. (2013) · arXiv (Cornell University) · doi:10.48550/arxiv.1306.4265 · Cited by 62 · see citing works
KANDINSKY Patterns as IQ-Test for Machine Learning
Holzinger, A., Kickmeier-Rust, M., & Müller, H. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-29726-8_1 · Cited by 28 · see citing works
An AI-Augmented Lesion Detection Framework For Liver Metastases With Model InterpretabilityPreprint
Hunt, X. J., Abbey, R., Tharrington, R., Huiskens, J., & Wesdorp, N. J. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1907.07713 · Cited by 4 · see citing works
Flagged first, then by citations
Research works printed but not linked: 1
- [2]2. Ancona, M., Ceolini, E., ztireli, C., Gross, M.: Towards better understanding of gradientbased attribution methods for deep neural networks. In: International Conference on Learning Representations (2018)
Cited by
Sorted by citations
Stepin, I., Alonso, J. M., Catala, A., & Pereira-Farina, M. (2021) · IEEE Access · doi:10.1109/access.2021.3051315 · Cited by 376 · see citing works
Shapley values for feature selection: the good, the bad, and the axioms
Fryer, D., Strumke, I., & Nguyen, H. (2021) · IEEE Access · doi:10.1109/access.2021.3119110 · Cited by 347 · see citing works
Problems with Shapley-value-based explanations as feature importance measures
Kumar, I. E., Venkatasubramanian, S., Scheidegger, C., & Friedler, S. (2020) · arXiv · doi:10.48550/arxiv.2002.11097 · Cited by 228 · see citing works
“If we are willing to consider intervention distributions (Section 2.2.1), then the framework provided by Merrick & Taly (2019) provides a slightly different contrastive explanation: in their setting, the Shapley value assignment can be thought of as a set of answers to the question, “Why f(x)…”
BackgroundMethodInfluentialKabir, S., Hossain, M. S., & Andersson, K. (2025) · Algorithms · doi:10.3390/a18090556 · Cited by 44 · see citing works
AI Data Readiness Inspector (AIDRIN) for Quantitative Assessment of Data Readiness for AI
Hiniduma, K., Byna, S., Bez, J. L., & Madduri, R. (2024) · Proceedings of the 36th International Conference on Scientific and Statistical Database Management · doi:10.1145/3676288.3676296 · Cited by 15 · see citing works
- Authors
- Merrick, L., & Taly, A.
- Corresponding author
- Merrick, Luke
- ORCID
- Taly, Ankur
- Venue
- Lecture notes in computer science
- Publisher
- Springer International Publishing
- Published
- 2020
- Volume
- LNCS-12279
- Pages
- 17-38
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W3038107861
- Cited by
- 277
- References
- 32
- Citations by year
- 2026: 37 · 2025: 68 · 2024: 72 · 2023: 34 · 2022: 31 · 2021: 26 · 2020: 8 · 2019: 1
- FWCI
- 9.27
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- Locations
- Lecture notes in computer science (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- HAL (submittedVersion, cc-by)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
- Physical Sciences › Computer Science › Software › Software Testing and Debugging Techniques
- Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
- Keywords
- Shapley value, explainable machine learning, feature attribution, SHAP, cooperative game theory, confidence intervals, contrastive explanations, cognitive psychology, black-box models, Lending Club
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review