Common Functional Decompositions Can Mis-attribute Differences in Outcomes Between Populations
Author
Abstract
Suggested Citation
Download full text from publisher
References listed on IDEAS
- Antoniadis, Anestis & Lambert-Lacroix, Sophie & Poggi, Jean-Michel, 2021. "Random forests for global sensitivity analysis: A selective review," Reliability Engineering and System Safety, Elsevier, vol. 206(C).
- Daniel W. Apley & Jingyu Zhu, 2020. "Visualizing the effects of predictor variables in black box supervised learning models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 82(4), pages 1059-1086, September.
- Kathryn E. Hill & Stuart C. Brown & Alice Jones & Damien Fordham & Robert S. Hill, 2023. "Modelling Climate Using Leaves of Nothofagus cunninghamii —Overcoming Confounding Factors," Sustainability, MDPI, vol. 15(9), pages 1-11, May.
Most related items
These are the items that most often cite the same works as this one and are cited by the same works as this one.- Ruairi C. Robertson & Thaddeus J. Edens & Lynnea Carr & Kuda Mutasa & Ethan K. Gough & Ceri Evans & Hyun Min Geum & Iman Baharmand & Sandeep K. Gill & Robert Ntozini & Laura E. Smith & Bernard Chasekw, 2023. "The gut microbiome and early-life growth in a population with high prevalence of stunting," Nature Communications, Nature, vol. 14(1), pages 1-15, December.
- Leandro Andrián & Oscar Mauricio Valencia, 2023. "Past the Tipping Point? Assessing Debt Overhang in Latin America and the Caribbean," IDB Publications (Book Chapters), in: Andrew Powell & Oscar Mauricio Valencia (ed.), Dealing with Debt, edition 1, chapter 8, pages 183-196, Inter-American Development Bank.
- Lu, Xuefei & Borgonovo, Emanuele, 2023. "Global sensitivity analysis in epidemiological modeling," European Journal of Operational Research, Elsevier, vol. 304(1), pages 9-24.
- Kim, Jun Young & Kim, Dongjae & Li, Zezhong John & Dariva, Claudio & Cao, Yankai & Ellis, Naoko, 2023. "Predicting and optimizing syngas production from fluidized bed biomass gasifiers: A machine learning approach," Energy, Elsevier, vol. 263(PC).
- Jian Guo & Saizhuo Wang & Lionel M. Ni & Heung-Yeung Shum, 2022. "Quant 4.0: Engineering Quantitative Investment with Automated, Explainable and Knowledge-driven Artificial Intelligence," Papers 2301.04020, arXiv.org.
- Cao, Jason & Tao, Tao, 2025. "Can an identified environmental correlate of car ownership serve as a practical planning tool?," Transportation Research Part A: Policy and Practice, Elsevier, vol. 191(C).
- Mehdi Dasineh & Amir Ghaderi & Mohammad Bagherzadeh & Mohammad Ahmadi & Alban Kuriqi, 2021. "Prediction of Hydraulic Jumps on a Triangular Bed Roughness Using Numerical Modeling and Soft Computing Methods," Mathematics, MDPI, vol. 9(23), pages 1-24, December.
- Hu'e Sullivan & Hurlin Christophe & P'erignon Christophe & Saurin S'ebastien, 2022. "Measuring the Driving Forces of Predictive Performance: Application to Credit Scoring," Papers 2212.05866, arXiv.org, revised Jan 2025.
- Zhang, Chanyuan (Abigail) & Cho, Soohyun & Vasarhelyi, Miklos, 2022. "Explainable Artificial Intelligence (XAI) in auditing," International Journal of Accounting Information Systems, Elsevier, vol. 46(C).
- Bastos, João A. & Matos, Sara M., 2022.
"Explainable models of credit losses,"
European Journal of Operational Research, Elsevier, vol. 301(1), pages 386-394.
- João A. Bastos & Sara M. Matos, 2021. "Explainable models of credit losses," Working Papers REM 2021/0161, ISEG - Lisbon School of Economics and Management, REM, Universidade de Lisboa.
- Daniel Borup & Philippe Goulet Coulombe & Erik Christian Montes Schütte & David E. Rapach & Sander Schwenk-Nebbe, 2022.
"The Anatomy of Out-of-Sample Forecasting Accuracy,"
FRB Atlanta Working Paper
2022-16, Federal Reserve Bank of Atlanta.
- Daniel Borup & Philippe Goulet Coulombe & Erik Christian Montes Schütte & David E. Rapach & Sander Schwenk-Nebbe, 2024. "The Anatomy of Out-of-Sample Forecasting Accuracy," FRB Atlanta Working Paper 2022-16b, Federal Reserve Bank of Atlanta.
- Jeetesh Sharma & Murari Lal Mittal & Gunjan Soni, 2024. "Condition-based maintenance using machine learning and role of interpretability: a review," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 15(4), pages 1345-1360, April.
- Torii, André Jacomel & Novotny, Antonio André, 2021. "A priori error estimates for local reliability-based sensitivity analysis with Monte Carlo Simulation," Reliability Engineering and System Safety, Elsevier, vol. 213(C).
- Hu, Songhua & Xiong, Chenfeng & Ji, Ya & Wu, Xin & Liu, Kailun & Schonfeld, Paul, 2024. "Understanding factors influencing user engagement in incentive-based travel demand management program," Transportation Research Part A: Policy and Practice, Elsevier, vol. 186(C).
- Shao, Qifan & Zhang, Wenjia & Cao, Xinyu (Jason) & Yang, Jiawen, 2023. "Built environment interventions for emission mitigation: A machine learning analysis of travel-related CO2 in a developing city," Journal of Transport Geography, Elsevier, vol. 110(C).
- Petros C. Lazaridis & Ioannis E. Kavvadias & Konstantinos Demertzis & Lazaros Iliadis & Lazaros K. Vasiliadis, 2023. "Interpretable Machine Learning for Assessing the Cumulative Damage of a Reinforced Concrete Frame Induced by Seismic Sequences," Sustainability, MDPI, vol. 15(17), pages 1-31, August.
- Jung, WoongHee & Taflanidis, Alexandros A., 2023. "Efficient global sensitivity analysis for high-dimensional outputs combining data-driven probability models and dimensionality reduction," Reliability Engineering and System Safety, Elsevier, vol. 231(C).
- Chris Reimann, 2024. "Predicting financial crises: an evaluation of machine learning algorithms and model explainability for early warning systems," Review of Evolutionary Political Economy, Springer, vol. 5(1), pages 51-83, June.
- Hu, Songhua & Xiong, Chenfeng & Chen, Peng & Schonfeld, Paul, 2023. "Examining nonlinearity in population inflow estimation using big data: An empirical comparison of explainable machine learning models," Transportation Research Part A: Policy and Practice, Elsevier, vol. 174(C).
- Wang, Ning & Xu, Yan & Wang, Sutong, 2022. "Interpretable boosting tree ensemble method for multisource building fire loss prediction," Reliability Engineering and System Safety, Elsevier, vol. 225(C).
More about this item
NEP fields
This paper has been announced in the following NEP Reports:- NEP-ECM-2025-05-12 (Econometrics)
Statistics
Access and download statisticsCorrections
All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:arx:papers:2504.16864. See general information about how to correct material in RePEc.
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: arXiv administrators (email available below). General contact details of provider: http://arxiv.org/ .
Please note that corrections may take a couple of weeks to filter through the various RePEc services.