IDEAS home Printed from https://ideas.repec.org/a/spr/advdac/v14y2020i4d10.1007_s11634-020-00418-3.html
   My bibliography  Save this article

A comparison of instance-level counterfactual explanation algorithms for behavioral and textual data: SEDC, LIME-C and SHAP-C

Author

Listed:
  • Yanou Ramon

    (University of Antwerp)

  • David Martens

    (University of Antwerp)

  • Foster Provost

    (Stern School of Business)

  • Theodoros Evgeniou

    (INSEAD)

Abstract

Predictive systems based on high-dimensional behavioral and textual data have serious comprehensibility and transparency issues: linear models require investigating thousands of coefficients, while the opaqueness of nonlinear models makes things worse. Counterfactual explanations are becoming increasingly popular for generating insight into model predictions. This study aligns the recently proposed linear interpretable model-agnostic explainer and Shapley additive explanations with the notion of counterfactual explanations, and empirically compares the effectiveness and efficiency of these novel algorithms against a model-agnostic heuristic search algorithm for finding evidence counterfactuals using 13 behavioral and textual data sets. We show that different search methods have different strengths, and importantly, that there is much room for future research.

Suggested Citation

  • Yanou Ramon & David Martens & Foster Provost & Theodoros Evgeniou, 2020. "A comparison of instance-level counterfactual explanation algorithms for behavioral and textual data: SEDC, LIME-C and SHAP-C," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 14(4), pages 801-819, December.
    • Handle: RePEc:spr:advdac:v:14:y:2020:i:4:d:10.1007_s11634-020-00418-3
    DOI: 10.1007/s11634-020-00418-3
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11634-020-00418-3
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s11634-020-00418-3?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Martens, David & Baesens, Bart & Van Gestel, Tony & Vanthienen, Jan, 2007. "Comprehensible credit scoring models using rule extraction from support vector machines," European Journal of Operational Research, Elsevier, vol. 183(3), pages 1466-1476, December.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Carrizosa, Emilio & Ramírez-Ayerbe, Jasone & Romero Morales, Dolores, 2024. "Mathematical optimization modelling for group counterfactual explanations," European Journal of Operational Research, Elsevier, vol. 319(2), pages 399-412.
    2. Emilio Carrizosa & Cristina Molero-Río & Dolores Romero Morales, 2021. "Mathematical optimization in classification and regression trees," TOP: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 29(1), pages 5-33, April.
    3. De Bock, Koen W. & Coussement, Kristof & Caigny, Arno De & Słowiński, Roman & Baesens, Bart & Boute, Robert N. & Choi, Tsan-Ming & Delen, Dursun & Kraus, Mathias & Lessmann, Stefan & Maldonado, Sebast, 2024. "Explainable AI for Operational Research: A defining framework, methods, applications, and a research agenda," European Journal of Operational Research, Elsevier, vol. 317(2), pages 249-272.
    4. Emilio Carrizosa & Jasone Ramírez-Ayerbe & Dolores Romero Morales, 2024. "A new model for counterfactual analysis for functional data," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 18(4), pages 981-1000, December.
    5. de Oliveira, Raphael Mazzine Barbosa & Sörensen, Kenneth & Martens, David, 2024. "A model-agnostic and data-independent tabu search algorithm to generate counterfactuals for tabular, image, and text data," European Journal of Operational Research, Elsevier, vol. 317(2), pages 286-302.
    6. Tadeusz A. Grzeszczyk & Michal K. Grzeszczyk, 2022. "Justifying Short-Term Load Forecasts Obtained with the Use of Neural Models," Energies, MDPI, vol. 15(5), pages 1-20, March.

    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.
    1. Li, Yibei & Wang, Ximei & Djehiche, Boualem & Hu, Xiaoming, 2020. "Credit scoring by incorporating dynamic networked information," European Journal of Operational Research, Elsevier, vol. 286(3), pages 1103-1112.
    2. Loterman, Gert & Brown, Iain & Martens, David & Mues, Christophe & Baesens, Bart, 2012. "Benchmarking regression algorithms for loss given default modeling," International Journal of Forecasting, Elsevier, vol. 28(1), pages 161-170.
    3. Yu, Lean & Wang, Shouyang & Lai, Kin Keung, 2009. "An intelligent-agent-based fuzzy group decision making model for financial multicriteria decision support: The case of credit scoring," European Journal of Operational Research, Elsevier, vol. 195(3), pages 942-959, June.
    4. Nadia Ayed & Khemaies Bougatef, 2024. "Performance Assessment of Logistic Regression (LR), Artificial Neural Network (ANN), Fuzzy Inference System (FIS) and Adaptive Neuro-Fuzzy System (ANFIS) in Predicting Default Probability: The Case of," Computational Economics, Springer;Society for Computational Economics, vol. 64(3), pages 1803-1835, September.
    5. Juan Laborda & Seyong Ryoo, 2021. "Feature Selection in a Credit Scoring Model," Mathematics, MDPI, vol. 9(7), pages 1-22, March.
    6. Liao, Jui-Jung & Shih, Ching-Hui & Chen, Tai-Feng & Hsu, Ming-Fu, 2014. "An ensemble-based model for two-class imbalanced financial problem," Economic Modelling, Elsevier, vol. 37(C), pages 175-183.
    7. Dejaeger, Karel & Goethals, Frank & Giangreco, Antonio & Mola, Lapo & Baesens, Bart, 2012. "Gaining insight into student satisfaction using comprehensible data mining techniques," European Journal of Operational Research, Elsevier, vol. 218(2), pages 548-562.
    8. Janssens, Bram & Schetgen, Lisa & Bogaert, Matthias & Meire, Matthijs & Van den Poel, Dirk, 2024. "360 Degrees rumor detection: When explanations got some explaining to do," European Journal of Operational Research, Elsevier, vol. 317(2), pages 366-381.
    9. K. W. De Bock & D. Van Den Poel, 2012. "Reconciling Performance and Interpretability in Customer Churn Prediction using Ensemble Learning based on Generalized Additive Models," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 12/805, Ghent University, Faculty of Economics and Business Administration.
    10. Przemys{l}aw Biecek & Marcin Chlebus & Janusz Gajda & Alicja Gosiewska & Anna Kozak & Dominik Ogonowski & Jakub Sztachelski & Piotr Wojewnik, 2021. "Enabling Machine Learning Algorithms for Credit Scoring -- Explainable Artificial Intelligence (XAI) methods for clear understanding complex predictive models," Papers 2104.06735, arXiv.org.
    11. Pelin Ayranci & Phung Lai & Nhathai Phan & Han Hu & Alexander Kolinowski & David Newman & Deijing Dou, 2022. "OnML: an ontology-based approach for interpretable machine learning," Journal of Combinatorial Optimization, Springer, vol. 44(1), pages 770-793, August.
    12. B Baesens & C Mues & D Martens & J Vanthienen, 2009. "50 years of data mining and OR: upcoming trends and challenges," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 60(1), pages 16-23, May.
    13. DE CNUDDE, Sofie & MARTENS, David & PROVOST, Foster, 2018. "An exploratory study towards applying and demystifying deep learning classification on behavioral big data," Working Papers 2018002, University of Antwerp, Faculty of Business and Economics.
    14. Pedro Duarte Silva, A., 2017. "Optimization approaches to Supervised Classification," European Journal of Operational Research, Elsevier, vol. 261(2), pages 772-788.
    15. Stefan Lessmann & Stefan Voß, 2010. "Customer-Centric Decision Support," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 2(2), pages 79-93, April.
    16. Blanquero, Rafael & Carrizosa, Emilio & Molero-Río, Cristina & Morales, Dolores Romero, 2022. "On sparse optimal regression trees," European Journal of Operational Research, Elsevier, vol. 299(3), pages 1045-1054.
    17. Coussement, Kristof & Buckinx, Wouter, 2011. "A probability-mapping algorithm for calibrating the posterior probabilities: A direct marketing application," European Journal of Operational Research, Elsevier, vol. 214(3), pages 732-738, November.
    18. DE CNUDDE, Sofie & MOEYERSOMS, Julie & STANKOVA, Marija & TOBBACK, Ellen & JAVALY, Vinayak & MARTENS, David, 2015. "Who cares about your Facebook friends? Credit scoring for microfinance," Working Papers 2015018, University of Antwerp, Faculty of Business and Economics.
    19. Dimitris Andriosopoulos & Michalis Doumpos & Panos M. Pardalos & Constantin Zopounidis, 2019. "Computational approaches and data analytics in financial services: A literature review," Journal of the Operational Research Society, Taylor & Francis Journals, vol. 70(10), pages 1581-1599, October.
    20. TOBBACK, Ellen & MARTENS, David & VAN GESTEL, Tony & BAESENS, Bart, 2012. "Forecasting loss given default models: Impact of account characteristics and the macroeconomic state," Working Papers 2012019, University of Antwerp, Faculty of Business and Economics.

    Corrections

    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:spr:advdac:v:14:y:2020:i:4:d:10.1007_s11634-020-00418-3. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.