Overview and practical recommendations on using Shapley Values for identifying predictive biomarkers via CATE modeling | Paper Citation Record

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This paper cites DigitalFinance 2021;3:99–148.

Overview and practical recommendations on using Shapley Values for identifying predictive biomarkers via CATE modeling DigitalFinance 2021;3:99–148

Reference 1

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This paper cites ProceedingsofMachineLearning Research2017: 1–13.

Overview and practical recommendations on using Shapley Values for identifying predictive biomarkers via CATE modeling ProceedingsofMachineLearning Research2017: 1–13

Reference 2

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This paper cites Towards optimal doubly robust estimation of heterogeneous causal effects.Electronic Journal of Statistics 2023; 17(2): 3008–3049.

Overview and practical recommendations on using Shapley Values for identifying predictive biomarkers via CATE modeling Towards optimal doubly robust estimation of heterogeneous causal effects.Electronic Journal of Statistics 2023; 17(2): 3008–3049

Reference 3

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This paper cites Subgroup identification in clinical trials: an overview of available methods and their implementations with R.Annals of translational medicine2018; 6(7).

Overview and practical recommendations on using Shapley Values for identifying predictive biomarkers via CATE modeling Subgroup identification in clinical trials: an overview of available methods and their implementations with R.Annals of translational medicine2018; 6(7)

Reference 5

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This paper cites Modern approaches for evaluating treatment effect heterogeneity from clinical trials and observational data.Statistics In Medicine2024; 43(22): 4388-4436.

Overview and practical recommendations on using Shapley Values for identifying predictive biomarkers via CATE modeling Modern approaches for evaluating treatment effect heterogeneity from clinical trials and observational data.Statistics In Medicine2024; 43(22): 4388-4436

Reference 6

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This paper cites Biometrics2017; 73: 1199-1209.

Overview and practical recommendations on using Shapley Values for identifying predictive biomarkers via CATE modeling Biometrics2017; 73: 1199-1209

Reference 7

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This paper cites Metalearners for estimating heterogeneous treatment effects using machine learning.

Overview and practical recommendations on using Shapley Values for identifying predictive biomarkers via CATE modeling Metalearners for estimating heterogeneous treatment effects using machine learning

Reference 8

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This paper cites Cross-Fitting and Averaging for Machine Learning Estimation of Heterogeneous Treatment Effects.

Overview and practical recommendations on using Shapley Values for identifying predictive biomarkers via CATE modeling Cross-Fitting and Averaging for Machine Learning Estimation of Heterogeneous Treatment Effects

Reference 9

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This paper cites Subgroup identification using the personalized package.Journal of Statistical Software2021; 98(5): 1-–60.

Overview and practical recommendations on using Shapley Values for identifying predictive biomarkers via CATE modeling Subgroup identification using the personalized package.Journal of Statistical Software2021; 98(5): 1-–60

Reference 10

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This paper cites ObservationalStudies 2016;5(2):37–51.

Overview and practical recommendations on using Shapley Values for identifying predictive biomarkers via CATE modeling ObservationalStudies 2016;5(2):37–51

Reference 11

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This paper cites Some methods for heterogeneous treatment effect estimation in high dimensions.Statistics in Medicine2018; 37(11): 1767–1787.

Overview and practical recommendations on using Shapley Values for identifying predictive biomarkers via CATE modeling Some methods for heterogeneous treatment effect estimation in high dimensions.Statistics in Medicine2018; 37(11): 1767–1787

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:29:16.786454Z

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This paper cites CRAN brf package: Causal Inference for a Binary Treatment and Continuous Outcome using Bayesian Causal Forests.

Overview and practical recommendations on using Shapley Values for identifying predictive biomarkers via CATE modeling CRAN brf package: Causal Inference for a Binary Treatment and Continuous Outcome using Bayesian Causal Forests

Reference 13

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This paper cites Predictive biomarker identification for biopharmaceutical development.Statistics in Biopharmaceutical Research2021; 13(2): 239–247.

Overview and practical recommendations on using Shapley Values for identifying predictive biomarkers via CATE modeling Predictive biomarker identification for biopharmaceutical development.Statistics in Biopharmaceutical Research2021; 13(2): 239–247

Reference 14

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Overview and practical recommendations on using Shapley Values for identifying predictive biomarkers via CATE modeling Unresolved cited work

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This paper cites DataMining Knowl Discov2019; 9(5).

Overview and practical recommendations on using Shapley Values for identifying predictive biomarkers via CATE modeling DataMining Knowl Discov2019; 9(5)

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This paper cites 35thConferenceonNeuralInformationProcessingSystems(NeurIPS), Track on Datasets and Benchmarks2021.

Overview and practical recommendations on using Shapley Values for identifying predictive biomarkers via CATE modeling 35thConferenceonNeuralInformationProcessingSystems(NeurIPS), Track on Datasets and Benchmarks2021

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:29:16.755081Z

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This paper cites WATCH: A Workflow to Assess Treatment Effect Heterogeneity in Drug Development for Clinical Trial Sponsors.Pharmaceutical Statistics2025; 24(2): e2463.

Overview and practical recommendations on using Shapley Values for identifying predictive biomarkers via CATE modeling WATCH: A Workflow to Assess Treatment Effect Heterogeneity in Drug Development for Clinical Trial Sponsors.Pharmaceutical Statistics2025; 24(2): e2463

Reference 18

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Observation 77befcd9-6815-4b7d-923e-fb53fbb1d81e · outbound

This paper cites Distinguishing prognostic and predictive biomarkers: an information theoretic approach.Bioinformatics 2018; 34(23).

Overview and practical recommendations on using Shapley Values for identifying predictive biomarkers via CATE modeling Distinguishing prognostic and predictive biomarkers: an information theoretic approach.Bioinformatics 2018; 34(23)

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This paper cites Springer New York, NY.

Overview and practical recommendations on using Shapley Values for identifying predictive biomarkers via CATE modeling Springer New York, NY

Reference 20

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This paper cites A unified approach to interpreting model predictions..Advances in Neural Information Processing Systems2017; 30.

Overview and practical recommendations on using Shapley Values for identifying predictive biomarkers via CATE modeling A unified approach to interpreting model predictions..Advances in Neural Information Processing Systems2017; 30

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This paper cites Applied Causal Inference Powered by ML and AI.

Overview and practical recommendations on using Shapley Values for identifying predictive biomarkers via CATE modeling Applied Causal Inference Powered by ML and AI

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This paper cites In: International Committee on Computational Linguistics.

Overview and practical recommendations on using Shapley Values for identifying predictive biomarkers via CATE modeling In: International Committee on Computational Linguistics

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This paper cites Causal Shapley Values: Exploiting Causal Knowledge to Explain Individual Predictions of Complex Models.

Overview and practical recommendations on using Shapley Values for identifying predictive biomarkers via CATE modeling Causal Shapley Values: Exploiting Causal Knowledge to Explain Individual Predictions of Complex Models

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This paper cites Variable importance measures for heterogeneous causal effects.arXiv preprint arXiv:2204.060302022.

Overview and practical recommendations on using Shapley Values for identifying predictive biomarkers via CATE modeling Variable importance measures for heterogeneous causal effects.arXiv preprint arXiv:2204.060302022

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This paper cites Subgroup identification from randomized clinical trial data.Statistics in Medicine2011; 30(24): 2867–2880.

Overview and practical recommendations on using Shapley Values for identifying predictive biomarkers via CATE modeling Subgroup identification from randomized clinical trial data.Statistics in Medicine2011; 30(24): 2867–2880

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This paper cites an unresolved cited work.

Overview and practical recommendations on using Shapley Values for identifying predictive biomarkers via CATE modeling Unresolved cited work

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Observation b4654e9d-7829-436c-a258-3ce8f5719974 · outbound

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Overview and practical recommendations on using Shapley Values for identifying predictive biomarkers via CATE modeling Bagging predictors.Machine learning1996; 24(2): 123–140

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This paper cites TheAnnalsofAppliedStatistics 2010; 4(1): 266 – 298.

Overview and practical recommendations on using Shapley Values for identifying predictive biomarkers via CATE modeling TheAnnalsofAppliedStatistics 2010; 4(1): 266 – 298

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This paper cites Classification and regression trees.Wiley interdisciplinary reviews: data mining and knowledge discovery2011; 1(1): 14–23.

Overview and practical recommendations on using Shapley Values for identifying predictive biomarkers via CATE modeling Classification and regression trees.Wiley interdisciplinary reviews: data mining and knowledge discovery2011; 1(1): 14–23

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Overview and practical recommendations on using Shapley Values for identifying predictive biomarkers via CATE modeling Estimating causal effects of treatments in randomized and nonrandomized studies.Journal of Educational Psychology1974; 66(5): 688–701

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Overview and practical recommendations on using Shapley Values for identifying predictive biomarkers via CATE modeling Generic machine learning inference on heterogeneous treatment effects in randomized experiments, with an application to immunization in India.NBER Working Paper2018(No

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Overview and practical recommendations on using Shapley Values for identifying predictive biomarkers via CATE modeling Unresolved cited work

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This paper cites WIREsData Mining and Knowledge Discovery2019; 9(5): e1326.

Overview and practical recommendations on using Shapley Values for identifying predictive biomarkers via CATE modeling WIREsData Mining and Knowledge Discovery2019; 9(5): e1326

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Observation e05e6d22-4332-4fd7-b28e-af87570948be · outbound

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Overview and practical recommendations on using Shapley Values for identifying predictive biomarkers via CATE modeling Validating Causal Inference Models via Influence Functions

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Overview and practical recommendations on using Shapley Values for identifying predictive biomarkers via CATE modeling StatisticsinBiopharmaceuticalResearch 2015; 7(3): 214–229

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Overview and practical recommendations on using Shapley Values for identifying predictive biomarkers via CATE modeling Generalized random forests.The Annals of Statistics2019; 47(2): 1148–1178

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This paper cites doi: https://doi.org/10.1214/19-BA1195.

Overview and practical recommendations on using Shapley Values for identifying predictive biomarkers via CATE modeling doi: https://doi.org/10.1214/19-BA1195

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Observation 33efef2a-d4c7-466c-a1b4-3f864e3c7137 · outbound

This paper cites On discovering treatment-effect modifiers using Virtual Twins and Causal Forest ML in the presence of prognostic biomarkers.

Overview and practical recommendations on using Shapley Values for identifying predictive biomarkers via CATE modeling On discovering treatment-effect modifiers using Virtual Twins and Causal Forest ML in the presence of prognostic biomarkers

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Observation 2c04a615-60fa-4857-aaee-a6c972949c41 · outbound

This paper cites rlearner: Quasi-Oracle Estimation of Heterogeneous Treatment Effects.

Overview and practical recommendations on using Shapley Values for identifying predictive biomarkers via CATE modeling rlearner: Quasi-Oracle Estimation of Heterogeneous Treatment Effects

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Source-reported events for the cited work

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Overview and practical recommendations on using Shapley Values for identifying predictive biomarkers via CATE modeling Root- N-Consistent Semiparametric Regression.Econometrica1988; 56(4): 931–54

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Source-reported events for the cited work

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Observation 726bd69f-0b3a-4e66-89fe-e4eb56cd18ad · outbound

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Overview and practical recommendations on using Shapley Values for identifying predictive biomarkers via CATE modeling Recursive partitioning for heterogeneous causal effects.Proceedings of the National Academy of Sciences 2016; 113(27): 7353–7360

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Source-reported events for the cited work

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Overview and practical recommendations on using Shapley Values for identifying predictive biomarkers via CATE modeling A simple method for estimating interactions between a treatment and a large number of covariates.Journal of the American Statistical Association2014; 109(508): 1517–1532

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Overview and practical recommendations on using Shapley Values for identifying predictive biomarkers via CATE modeling M.Interpretable Machine Learning: A Guide for Making Black Box Models Explainable

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Source-reported events for the cited work

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Observation 12543eb3-54c8-4e84-9cac-bb7fbca08f2c · outbound

This paper cites an unresolved cited work.

Overview and practical recommendations on using Shapley Values for identifying predictive biomarkers via CATE modeling Unresolved cited work

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This paper cites Whyshoulditrustyou?.

Overview and practical recommendations on using Shapley Values for identifying predictive biomarkers via CATE modeling Whyshoulditrustyou?

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This paper cites Deep inside convolutional networks: visualising image classification models and saliency maps.

Overview and practical recommendations on using Shapley Values for identifying predictive biomarkers via CATE modeling Deep inside convolutional networks: visualising image classification models and saliency maps

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No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Overview and practical recommendations on using Shapley Values for identifying predictive biomarkers via CATE modeling 2017: 3145–3153

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Source-reported events for the cited work

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Observation 31d93383-789b-4180-aacb-14ec94bd7056 · outbound

This paper cites Grad-cam: Visual explanations from deep networks via gradient-based localization.

Overview and practical recommendations on using Shapley Values for identifying predictive biomarkers via CATE modeling Grad-cam: Visual explanations from deep networks via gradient-based localization

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Overview and practical recommendations on using Shapley Values for identifying predictive biomarkers via CATE modeling SmoothGrad: removing noise by adding noise

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Observation 2a881ebf-f433-4530-93cb-58c4b54cc87d · outbound

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Overview and practical recommendations on using Shapley Values for identifying predictive biomarkers via CATE modeling A Guide for Making Black Box Models Explainable

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:29:16.399564Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 232ea52c-e60d-4925-89cf-223bbb1e9f32 · outbound

This paper cites A Value for n-Person Games: 307–318; Princeton: Princeton University Press.

Overview and practical recommendations on using Shapley Values for identifying predictive biomarkers via CATE modeling A Value for n-Person Games: 307–318; Princeton: Princeton University Press

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:29:16.385380Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 01160bf8-a319-4390-a9c3-fb9123f752d8 · outbound

This paper cites The many Shapley values for model explanation.

Overview and practical recommendations on using Shapley Values for identifying predictive biomarkers via CATE modeling The many Shapley values for model explanation

Reference 53

Resolution: verified exact; local_arxiv, observed 2026-08-16T04:29:15.878968Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 81f86397-9bd8-449d-8dfa-f6a51bcc7c07 · outbound

This paper cites Greedy function approximation: a gradient boosting machine.Annals of statistics2001: 1189–1232.

Overview and practical recommendations on using Shapley Values for identifying predictive biomarkers via CATE modeling Greedy function approximation: a gradient boosting machine.Annals of statistics2001: 1189–1232

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:29:16.371421Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 9e06c110-a199-49b2-96d2-404322454dab · outbound

This paper cites Nature machine intelligence2020; 2(1): 56–67.

Overview and practical recommendations on using Shapley Values for identifying predictive biomarkers via CATE modeling Nature machine intelligence2020; 2(1): 56–67

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:29:16.356352Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 01323ecf-f5f2-46d4-881a-9c91acb24918 · outbound

This paper cites Improving the Sampling Strategy in KernelSHAP.arXiv preprint arXiv:2410.048832024.

Overview and practical recommendations on using Shapley Values for identifying predictive biomarkers via CATE modeling Improving the Sampling Strategy in KernelSHAP.arXiv preprint arXiv:2410.048832024

Reference 57

Resolution: unresolved; no resolver link, observed 2026-08-16T04:29:15.219518Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c692d266-a72c-4059-9b21-39aa28d0c458 · outbound

This paper cites Algorithms to estimate Shapley value feature attributions.Nature Machine Intelligence2023; 5(6): 590–601.

Overview and practical recommendations on using Shapley Values for identifying predictive biomarkers via CATE modeling Algorithms to estimate Shapley value feature attributions.Nature Machine Intelligence2023; 5(6): 590–601

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:29:16.340941Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation c4beef9a-c7cf-483c-a65b-3308c3b0eef2 · outbound

This paper cites an unresolved cited work.

Overview and practical recommendations on using Shapley Values for identifying predictive biomarkers via CATE modeling Unresolved cited work

Reference 59

Resolution: unresolved; raw_fallback, observed 2026-08-16T04:29:16.328268Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation e7907e94-b419-4f57-a018-de2a90a3d3c8 · outbound

This paper cites CRAN; CRAN: 2021.

Overview and practical recommendations on using Shapley Values for identifying predictive biomarkers via CATE modeling CRAN; CRAN: 2021

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:29:16.316527Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 82d95ce9-fbe9-4f55-89bb-389d22ae446c · outbound

This paper cites shapr: An R-package for explaining machine learning models with dependence-aware Shapley values.Journal of Open Source Software2019; 5(46): 2027.

Overview and practical recommendations on using Shapley Values for identifying predictive biomarkers via CATE modeling shapr: An R-package for explaining machine learning models with dependence-aware Shapley values.Journal of Open Source Software2019; 5(46): 2027

Reference 61

Resolution: verified exact; doi, observed 2026-08-16T04:29:15.446325Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation cef528af-8ec6-4e57-8b8e-eaa1c2a3a547 · outbound

This paper cites an unresolved cited work.

Overview and practical recommendations on using Shapley Values for identifying predictive biomarkers via CATE modeling Unresolved cited work

Reference 62

Resolution: unresolved; raw_fallback, observed 2026-08-16T04:29:16.304629Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 4955a500-79ec-4e37-815b-9cfc38ec6ef6 · outbound

This paper cites Model-agnostic interpretability with shapley values.

Overview and practical recommendations on using Shapley Values for identifying predictive biomarkers via CATE modeling Model-agnostic interpretability with shapley values

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:29:16.286928Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation a7603091-ff5b-4eb4-906a-a6c341cb3933 · outbound

This paper cites SHAP-Based Explanation Methods: A Review for NLP Inter- pretability.

Overview and practical recommendations on using Shapley Values for identifying predictive biomarkers via CATE modeling SHAP-Based Explanation Methods: A Review for NLP Inter- pretability

Reference 64

Resolution: unresolved; no resolver link, observed 2026-08-16T04:29:15.252523Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3f4f41ea-711e-4074-89b4-ec4f99980204 · outbound

This paper cites Tabular data: Deep learning is not all you need.Information Fusion2022; 81: 84-90.

Overview and practical recommendations on using Shapley Values for identifying predictive biomarkers via CATE modeling Tabular data: Deep learning is not all you need.Information Fusion2022; 81: 84-90

Reference 65

Resolution: unresolved; no resolver link, observed 2026-08-16T04:29:15.258155Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c50f9355-87ed-443a-9883-dcb62e5894a1 · outbound

This paper cites Why do tree-based models still outperform deep learning on typical tabular data?.

Overview and practical recommendations on using Shapley Values for identifying predictive biomarkers via CATE modeling Why do tree-based models still outperform deep learning on typical tabular data?

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:29:16.266746Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 2cdb654d-4338-45ab-8e13-4727616a9216 · outbound

This paper cites CRAN; CRAN: 2023.

Overview and practical recommendations on using Shapley Values for identifying predictive biomarkers via CATE modeling CRAN; CRAN: 2023

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:29:16.239146Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 25c9601d-cd4d-489e-8806-21845e8453f0 · outbound

This paper cites Classification and Regression by randomForest.R News2002; 2(3): 18-22.

Overview and practical recommendations on using Shapley Values for identifying predictive biomarkers via CATE modeling Classification and Regression by randomForest.R News2002; 2(3): 18-22

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:29:16.225959Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 436ab090-4b4b-436d-a843-bb6ce8e9cb36 · outbound

This paper cites XGBoost: A Scalable Tree Boosting System.

Overview and practical recommendations on using Shapley Values for identifying predictive biomarkers via CATE modeling XGBoost: A Scalable Tree Boosting System

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:29:16.214409Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 41f62dce-57e8-4115-a771-0f270872ae22 · outbound

This paper cites Overview of modern approaches for identifying and evaluating heterogeneous treatment effects from clinical data.Clinical Trials2023; 20(4).

Overview and practical recommendations on using Shapley Values for identifying predictive biomarkers via CATE modeling Overview of modern approaches for identifying and evaluating heterogeneous treatment effects from clinical data.Clinical Trials2023; 20(4)

Reference 70

Resolution: verified exact; doi, observed 2026-08-16T04:29:15.412890Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 8bb32b3b-84fa-4591-8726-96cb2e568d88 · outbound

This paper cites Random Forests of Interaction Trees for Estimating Individualized Treatment Effects in Randomized Trials.Statistics in Medicine2017; 37.

Overview and practical recommendations on using Shapley Values for identifying predictive biomarkers via CATE modeling Random Forests of Interaction Trees for Estimating Individualized Treatment Effects in Randomized Trials.Statistics in Medicine2017; 37

Reference 71

Resolution: verified exact; doi, observed 2026-08-16T04:29:15.386380Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 96b2bced-7beb-4f06-87c8-0907c0408d30 · outbound

This paper cites Acupuncture for chronic headache in primary care: large, pragmatic, randomised trial.

Overview and practical recommendations on using Shapley Values for identifying predictive biomarkers via CATE modeling Acupuncture for chronic headache in primary care: large, pragmatic, randomised trial

Reference 73

Resolution: metadata mismatch; raw_fallback, observed 2026-08-16T04:29:15.732691Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation ed7f8bac-972d-414e-905d-99381dc99c75 · outbound

This paper cites Model-based recursive partitioning for subgroup analyses.The International Journal of Biostatistics2016; 12(1): 45–63.

Overview and practical recommendations on using Shapley Values for identifying predictive biomarkers via CATE modeling Model-based recursive partitioning for subgroup analyses.The International Journal of Biostatistics2016; 12(1): 45–63

Reference 74

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:29:16.199774Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 57b0e9c4-00c0-40a6-a797-b8811958effb · outbound

This paper cites Distilling heterogeneous treatment effects: Stable subgroup estimation in causal inference.arxiv 2025.

Overview and practical recommendations on using Shapley Values for identifying predictive biomarkers via CATE modeling Distilling heterogeneous treatment effects: Stable subgroup estimation in causal inference.arxiv 2025

Reference 75

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:29:16.167126Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 06445499-a8b2-41f1-bb33-b619d2d8ac2f · outbound

This paper cites Experimental evaluation of individualized treatment rules.Journal of the American Statistical Association 2021; 0(0): 1-15.

Overview and practical recommendations on using Shapley Values for identifying predictive biomarkers via CATE modeling Experimental evaluation of individualized treatment rules.Journal of the American Statistical Association 2021; 0(0): 1-15

Reference 76

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:29:16.150851Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 038f2651-4404-4b81-9a62-37c7565e76d6 · outbound

This paper cites LLpowershap: logistic loss-based automated Shapley values feature selection method.BMC Medical Research Methodology2024; 24(1): 247.

Overview and practical recommendations on using Shapley Values for identifying predictive biomarkers via CATE modeling LLpowershap: logistic loss-based automated Shapley values feature selection method.BMC Medical Research Methodology2024; 24(1): 247

Reference 77

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:29:16.137445Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 7d403864-716d-4c53-b491-d40caa876d0d · outbound

This paper cites In: Neural Information Processing Systems.

Overview and practical recommendations on using Shapley Values for identifying predictive biomarkers via CATE modeling In: Neural Information Processing Systems

Reference 78

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:29:16.120609Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Coverage vector

Reference resolution

Outbound references

Pith citing papers