IDEAS home Printed from https://ideas.repec.org/a/bla/popmgt/v27y2018i4p696-716.html
   My bibliography  Save this article

The Forest or the Trees? Tackling Simpson's Paradox with Classification Trees

Author

Listed:
  • Galit Shmueli
  • Inbal Yahav

Abstract

Studying causal effects is central to research in operations management in manufacturing and services, from evaluating prevention procedures, to effects of policies and new operational technologies and practices. The growing availability of micro†level data creates challenges for researchers and decision makers in terms of choosing the right level of data aggregation for inference and decisions. Simpson's paradox describes the case where the direction of a causal effect is reversed in the aggregated data compared to the disaggregated data. Detecting whether Simpson's paradox occurs in a dataset used for decision making is therefore critical. This study introduces the use of Classification and Regression Trees for automated detection of potential Simpson's paradoxes in data with few or many potential confounding variables, and even with large samples (big data). Our approach relies on the tree structure and the location of the cause vs. the confounders in the tree. We discuss theoretical and computational aspects of the approach and illustrate it using several real applications in e†governance and healthcare.

Suggested Citation

  • Galit Shmueli & Inbal Yahav, 2018. "The Forest or the Trees? Tackling Simpson's Paradox with Classification Trees," Production and Operations Management, Production and Operations Management Society, vol. 27(4), pages 696-716, April.
  • Handle: RePEc:bla:popmgt:v:27:y:2018:i:4:p:696-716
    DOI: 10.1111/poms.12819
    as

    Download full text from publisher

    File URL: https://doi.org/10.1111/poms.12819
    Download Restriction: no

    File URL: https://libkey.io/10.1111/poms.12819?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
    ---><---

    Citations

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


    Cited by:

    1. Dieudonné Tchuente & Serge Nyawa, 2022. "Real estate price estimation in French cities using geocoding and machine learning," Annals of Operations Research, Springer, vol. 308(1), pages 571-608, January.
    2. Li, Jiawen & Meng, Lu & Zhang, Zelin & Yang, Kejia, 2023. "Low-frequency, high-impact: Discovering important rare events from UGC," Journal of Retailing and Consumer Services, Elsevier, vol. 70(C).
    3. Khosrowabadi, Naghmeh & Hoberg, Kai & Imdahl, Christina, 2022. "Evaluating human behaviour in response to AI recommendations for judgemental forecasting," European Journal of Operational Research, Elsevier, vol. 303(3), pages 1151-1167.
    4. Tomer Geva & Maytal Saar‐Tsechansky, 2021. "Who Is a Better Decision Maker? Data‐Driven Expert Ranking Under Unobserved Quality," Production and Operations Management, Production and Operations Management Society, vol. 30(1), pages 127-144, January.
    5. Xiangyu Chang & Yinghui Huang & Mei Li & Xin Bo & Subodha Kumar, 2021. "Efficient Detection of Environmental Violators: A Big Data Approach," Production and Operations Management, Production and Operations Management Society, vol. 30(5), pages 1246-1270, May.
    6. Singha, Sumanta & Arha, Himanshu & Kar, Arpan Kumar, 2023. "Healthcare analytics: A techno-functional perspective," Technological Forecasting and Social Change, Elsevier, vol. 197(C).
    7. Zhang, Jiayuan & Yalcin, Mehmet G. & Hales, Douglas N., 2021. "Elements of paradoxes in supply chain management literature: A systematic literature review," International Journal of Production Economics, Elsevier, vol. 232(C).

    More about this item

    Statistics

    Access and download statistics

    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:bla:popmgt:v:27:y:2018:i:4:p:696-716. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: Wiley Content Delivery (email available below). General contact details of provider: http://onlinelibrary.wiley.com/journal/10.1111/(ISSN)1937-5956 .

    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.