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Paper Citation Record · LEDGER

Beyond Shapley Values: Cooperative Games for the Interpretation of Machine Learning Models

As of 7 August 2026, this Paper Citation Record lists 27 of 27 outbound references and 1 inbound Pith citation observation for arXiv:2506.13900.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2506.13900 v1

Coverage vector

measured 27 of 27 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T00:32:56.074741Z

measured 28 of 28 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-06-29T19:07:32.564519Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: arxiv_reference, observed 2026-06-29T19:13:52.682699Z

Reference resolution

27 of 27 outbound references displayed

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External citation measurements

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Outbound references

Observation 937e7622-39fc-4f4c-8f8c-3c90c1f8857b · outbound

This paper cites Amoukou, Tangi Sala ¨un, and Nicolas Brunel.

Beyond Shapley Values: Cooperative Games for the Interpretation of Machine Learning Models Amoukou, Tangi Sala ¨un, and Nicolas Brunel

Reference 1

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Observation d569a06a-7712-4362-846f-34deda9c9719 · outbound

This paper cites Harsanyi.

Beyond Shapley Values: Cooperative Games for the Interpretation of Machine Learning Models Harsanyi

Reference 5

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This paper cites On the coalitional decomposition of parameters of interest.

Beyond Shapley Values: Cooperative Games for the Interpretation of Machine Learning Models On the coalitional decomposition of parameters of interest

Reference 8

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This paper cites A unified approach to interpreting model predictions.

Beyond Shapley Values: Cooperative Games for the Interpretation of Machine Learning Models A unified approach to interpreting model predictions

Reference 12

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Beyond Shapley Values: Cooperative Games for the Interpretation of Machine Learning Models Unresolved cited work

Reference 18

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This paper cites [Vasil’ev and van der Laan, 2001] Valeri Vasil’ev and Ger- ard van der Laan.

Beyond Shapley Values: Cooperative Games for the Interpretation of Machine Learning Models [Vasil’ev and van der Laan, 2001] Valeri Vasil’ev and Ger- ard van der Laan

Reference 22

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Observation 0537c462-7663-4aec-ab56-216289247cb7 · outbound

This paper cites [Watson et al., 2023] David Watson, Joshua O’Hara, Niek Tax, Richard Mudd, and Ido Guy.

Beyond Shapley Values: Cooperative Games for the Interpretation of Machine Learning Models [Watson et al., 2023] David Watson, Joshua O’Hara, Niek Tax, Richard Mudd, and Ido Guy

Reference 24

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Observation 98bac4f5-28f0-4d72-bb3e-731150c07928 · outbound

This paper cites Probabilistic values for games, page 101–120.

Beyond Shapley Values: Cooperative Games for the Interpretation of Machine Learning Models Probabilistic values for games, page 101–120

Reference 25

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Observation a7675f1c-fc10-49f2-98ff-4beb1e4e0abd · outbound

This paper cites Why You Should Not Trust Interpretations in Machine Learning: Adversarial Attacks on Partial Dependence Plots.

Beyond Shapley Values: Cooperative Games for the Interpretation of Machine Learning Models Why You Should Not Trust Interpretations in Machine Learning: Adversarial Attacks on Partial Dependence Plots

Reference 27

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This paper cites Nelson, and Jeremy Staum.

Beyond Shapley Values: Cooperative Games for the Interpretation of Machine Learning Models Nelson, and Jeremy Staum

Reference 1951

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Observation 955152a3-bf9d-4847-9c22-7c70bd1201b6 · outbound

This paper cites Haufe, R.

Beyond Shapley Values: Cooperative Games for the Interpretation of Machine Learning Models Haufe, R

Reference 1963

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This paper cites Why don’t xai techniques agree? characterizing the dis- agreements between post-hoc explanations of defect pre- dictions.

Beyond Shapley Values: Cooperative Games for the Interpretation of Machine Learning Models Why don’t xai techniques agree? characterizing the dis- agreements between post-hoc explanations of defect pre- dictions

Reference 1964

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Beyond Shapley Values: Cooperative Games for the Interpretation of Machine Learning Models Unresolved cited work

Reference 1988

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Beyond Shapley Values: Cooperative Games for the Interpretation of Machine Learning Models Unresolved cited work

Reference 1994

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Observation f75c693b-9a01-4c45-9fda-62067ba1ef5a · outbound

This paper cites Osborne and Ariel Rubinstein.

Beyond Shapley Values: Cooperative Games for the Interpretation of Machine Learning Models Osborne and Ariel Rubinstein

Reference 2000

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Observation 7d8ff54e-bd5e-4dbd-8486-82f26bdc1bb2 · outbound

This paper cites Verdinelli and L.

Beyond Shapley Values: Cooperative Games for the Interpretation of Machine Learning Models Verdinelli and L

Reference 2001

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Observation 3cf2d883-022c-44ef-87d6-b132b254ef69 · outbound

This paper cites The Many Shapley Values for Model Ex- planation.

Beyond Shapley Values: Cooperative Games for the Interpretation of Machine Learning Models The Many Shapley Values for Model Ex- planation

Reference 2010

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This paper cites On the foundations of combi- natorial theory I.

Beyond Shapley Values: Cooperative Games for the Interpretation of Machine Learning Models On the foundations of combi- natorial theory I

Reference 2014

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This paper cites An efficient explanation of individual classi- fications using game theory.

Beyond Shapley Values: Cooperative Games for the Interpretation of Machine Learning Models An efficient explanation of individual classi- fications using game theory

Reference 2016

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This paper cites Michael Ortmann.

Beyond Shapley Values: Cooperative Games for the Interpretation of Machine Learning Models Michael Ortmann

Reference 2017

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Observation 724c5f00-9e0f-448f-9bfb-7402fa9b1e7d · outbound

This paper cites Achieving interpretable machine learn- ing by functional decomposition of black-box models into explainable predictor effects,.

Beyond Shapley Values: Cooperative Games for the Interpretation of Machine Learning Models Achieving interpretable machine learn- ing by functional decomposition of black-box models into explainable predictor effects,

Reference 2019

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Observation c5593712-0bab-4faa-8c7e-10da16ffdbb1 · outbound

This paper cites Kernel-based anova decomposition and shapley effects – application to global sensitivity analysis,.

Beyond Shapley Values: Cooperative Games for the Interpretation of Machine Learning Models Kernel-based anova decomposition and shapley effects – application to global sensitivity analysis,

Reference 2020

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This paper cites Asymmetric shapley values: incorporating causal knowledge into model-agnostic explainability.

Beyond Shapley Values: Cooperative Games for the Interpretation of Machine Learning Models Asymmetric shapley values: incorporating causal knowledge into model-agnostic explainability

Reference 2021

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This paper cites Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI.

Beyond Shapley Values: Cooperative Games for the Interpretation of Machine Learning Models Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI

Reference 2022

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Observation da9d6e59-a6f1-45f0-9225-523d07743950 · outbound

This paper cites Hoeffding decomposition of functions of ran- dom dependent variables.

Beyond Shapley Values: Cooperative Games for the Interpretation of Machine Learning Models Hoeffding decomposition of functions of ran- dom dependent variables

Reference 2023

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Observation c421ff0b-19c1-427f-befb-b90552828da1 · outbound

This paper cites Explainable AI needs formalization.

Beyond Shapley Values: Cooperative Games for the Interpretation of Machine Learning Models Explainable AI needs formalization

Reference 2024

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This paper cites Shapley Effects For Sensitivity Analysis With Cor- related Inputs: Comparisons With Sobol’ Indices, Numer- ical Estimation And Applications.

Beyond Shapley Values: Cooperative Games for the Interpretation of Machine Learning Models Shapley Effects For Sensitivity Analysis With Cor- related Inputs: Comparisons With Sobol’ Indices, Numer- ical Estimation And Applications

Reference 2025

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Pith citing papers

Observation 1e09a978-f2c5-4efb-82c2-bc9a0f9ecb05 · inbound

Coalition Free Energy and Adaptive Precision in Multi-Agent Cooperation cites this paper.

Coalition Free Energy and Adaptive Precision in Multi-Agent Cooperation Beyond Shapley Values: Cooperative Games for the Interpretation of Machine Learning Models

Reference 25

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