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Causal Interpretations of Black-Box Models

Citations

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Cited by:

  1. Riccardo Di Francesco, 2022. "Aggregation Trees," CEIS Research Paper 546, Tor Vergata University, CEIS, revised 20 Nov 2023.
  2. Tuan-Anh Tran & Sushmita Sridhar & Stephen T. Reece & Octavie Lunguya & Jan Jacobs & Sandra Puyvelde & Florian Marks & Gordon Dougan & Nicholas R. Thomson & Binh T. Nguyen & Pham The Bao & Stephen Bak, 2024. "Combining machine learning with high-content imaging to infer ciprofloxacin susceptibility in isolates of Salmonella Typhimurium," Nature Communications, Nature, vol. 15(1), pages 1-15, December.
  3. Yamin Du & Huanhuan Cheng & Qing Liu & Song Tan, 2024. "The delayed and combinatorial response of online public opinion to the real world: An inquiry into news texts during the COVID-19 era," Palgrave Communications, Palgrave Macmillan, vol. 11(1), pages 1-18, December.
  4. Bansak, Kirk & Martén, Linna, 2024. "Algorithmic Decision-Making, Fairness, and the Distribution of Impact: Application to Refugee Matching," SOFI Working Papers in Labour Economics 6/2024, Stockholm University, Swedish Institute for Social Research.
  5. Mikko Tolkkinen & Saku Vaarala & Jukka Aroviita, 2021. "The Importance of Riparian Forest Cover to the Ecological Status of Agricultural Streams in a Nationwide Assessment," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(12), pages 4009-4020, September.
  6. Hansen, Sakina & Loftus, Joshua, 2023. "Model-agnostic auditing: a lost cause?," LSE Research Online Documents on Economics 120114, London School of Economics and Political Science, LSE Library.
  7. Julien Chevallier & Dominique Guégan & Stéphane Goutte, 2021. "Is It Possible to Forecast the Price of Bitcoin?," Forecasting, MDPI, vol. 3(2), pages 1-44, May.
  8. Katsafados, Apostolos G. & Leledakis, George N. & Panagiotou, Nikolaos P. & Pyrgiotakis, Emmanouil G., 2024. "Can central bankers’ talk predict bank stock returns? A machine learning approach," MPRA Paper 122899, University Library of Munich, Germany.
  9. Erkin Altuntas & Peter A. Gloor & Pascal Budner, 2022. "Measuring Ethical Values with AI for Better Teamwork," Future Internet, MDPI, vol. 14(5), pages 1-28, April.
  10. Gregory Gadzinski & Alessio Castello, 2022. "Combining white box models, black box machines and human interventions for interpretable decision strategies," Judgment and Decision Making, Society for Judgment and Decision Making, vol. 17(3), pages 598-627, May.
  11. Emilio Aguirre & Federico García-Suárez & Gabriela Sicilia, 2021. "Eficiencia técnica en la ganadería de carne bovina pastoril. Medición y exploración de sus determinantes en Uruguay," Documentos de Trabajo (working papers) 1321, Department of Economics - dECON.
  12. Schade, Philipp & Schuhmacher, Monika C., 2023. "Predicting entrepreneurial activity using machine learning," Journal of Business Venturing Insights, Elsevier, vol. 19(C).
  13. Chen, Yong & Lu, Zhiyuan & Liu, Heng & Wang, Hu & Zheng, Zunqing & Wang, Changhui & Sun, Xingyu & Xu, Linxun & Yao, Mingfa, 2024. "Machine learning-based design of target property-oriented fuels using explainable artificial intelligence," Energy, Elsevier, vol. 300(C).
  14. Islam, Towhidul & Meade, Nigel & Carson, Richard T. & Louviere, Jordan J. & Wang, Juan, 2022. "The usefulness of socio-demographic variables in predicting purchase decisions: Evidence from machine learning procedures," Journal of Business Research, Elsevier, vol. 151(C), pages 324-338.
  15. Stefano Cabras & J. D. Tena, 2023. "Implicit institutional incentives and individual decisions: Causal inference with deep learning models," Managerial and Decision Economics, John Wiley & Sons, Ltd., vol. 44(6), pages 3739-3754, September.
  16. M. Merz & R. Richman & T. Tsanakas & M. V. Wuthrich, 2021. "Interpreting Deep Learning Models with Marginal Attribution by Conditioning on Quantiles," Papers 2103.11706, arXiv.org.
  17. Lin Zhang & Suhong Zhou & Lanlan Qi & Yue Deng, 2022. "Nonlinear Effects of the Neighborhood Environments on Residents’ Mental Health," IJERPH, MDPI, vol. 19(24), pages 1-17, December.
  18. Anesti, Nikoleta & Kalamara, Eleni & Kapetanios, George, 2021. "Forecasting UK GDP growth with large survey panels," Bank of England working papers 923, Bank of England.
  19. Thomas R. Cook & Greg Gupton & Zach Modig & Nathan M. Palmer, 2021. "Explaining Machine Learning by Bootstrapping Partial Dependence Functions and Shapley Values," Research Working Paper RWP 21-12, Federal Reserve Bank of Kansas City.
  20. Amar Rao & Marco Tedeschi & Kamel Si Mohammed & Umer Shahzad, 2024. "Role of Economic Policy Uncertainty in Energy Commodities Prices Forecasting: Evidence from a Hybrid Deep Learning Approach," Computational Economics, Springer;Society for Computational Economics, vol. 64(6), pages 3295-3315, December.
  21. repec:cup:judgdm:v:17:y:2022:i:3:p:598-627 is not listed on IDEAS
  22. Loftus, Joshua R., 2024. "Position: the causal revolution needs scientific pragmatism," LSE Research Online Documents on Economics 125578, London School of Economics and Political Science, LSE Library.
  23. Borgonovo, Emanuele & Ghidini, Valentina & Hahn, Roman & Plischke, Elmar, 2023. "Explaining classifiers with measures of statistical association," Computational Statistics & Data Analysis, Elsevier, vol. 182(C).
  24. Madadkhani, Shiva & Ikonnikova, Svetlana, 2024. "Toward high-resolution projection of electricity prices: A machine learning approach to quantifying the effects of high fuel and CO2 prices," Energy Economics, Elsevier, vol. 129(C).
  25. Li Yao & He Ni, 2023. "Prediction of patent grant and interpreting the key determinants: an application of interpretable machine learning approach," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(9), pages 4933-4969, September.
  26. Wang, Gang-Jin & Chen, Yan & Zhu, You & Xie, Chi, 2024. "Systemic risk prediction using machine learning: Does network connectedness help prediction?," International Review of Financial Analysis, Elsevier, vol. 93(C).
  27. Jing Liu & Chao Zang & Qiting Zuo & Chunhui Han & Stefan Krause, 2023. "Application and Comparison of Different Models for Quantifying the Aquatic Community in a Dam-Controlled River," IJERPH, MDPI, vol. 20(5), pages 1-16, February.
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