IDEAS home Printed from https://ideas.repec.org/a/spr/astaws/v16y2022i3d10.1007_s11943-022-00311-9.html
   My bibliography  Save this article

Causality in statistics and data science education

Author

Listed:
  • Kevin Cummiskey

    (United States Military Academy)

  • Karsten Lübke

    (FOM University of Applied Sciences)

Abstract

Statisticians and data scientists transform raw data into understanding and insight. Ideally, these insights empower people to act and make better decisions. However, data is often misleading especially when trying to draw conclusions about causality (for example, Simpson’s paradox). Therefore, developing causal thinking in undergraduate statistics and data science programs is important. However, there is very little guidance in the education literature about what topics and learning outcomes, specific to causality, are most important. In this paper, we propose a causality curriculum for undergraduate statistics and data science programs. Students should be able to think causally, which is defined as a broad pattern of thinking that enables individuals to appropriately assess claims of causality based upon statistical evidence. They should understand how the data generating process affects their conclusions and how to incorporate knowledge from subject matter experts in areas of application. Important topics in causality for the undergraduate curriculum include the potential outcomes framework and counterfactuals, measures of association versus causal effects, confounding, causal diagrams, and methods for estimating causal effects.

Suggested Citation

  • Kevin Cummiskey & Karsten Lübke, 2022. "Causality in statistics and data science education," AStA Wirtschafts- und Sozialstatistisches Archiv, Springer;Deutsche Statistische Gesellschaft - German Statistical Society, vol. 16(3), pages 277-286, December.
  • Handle: RePEc:spr:astaws:v:16:y:2022:i:3:d:10.1007_s11943-022-00311-9
    DOI: 10.1007/s11943-022-00311-9
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11943-022-00311-9
    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/s11943-022-00311-9?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. Andrew Gelman & Aki Vehtari, 2021. "What are the Most Important Statistical Ideas of the Past 50 Years?," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 116(536), pages 2087-2097, October.
    2. Jessica Utts, 2021. "Enhancing Data Science Ethics Through Statistical Education and Practice," International Statistical Review, International Statistical Institute, vol. 89(1), pages 1-17, April.
    3. Daniel Kaplan, 2018. "Teaching Stats for Data Science," The American Statistician, Taylor & Francis Journals, vol. 72(1), pages 89-96, January.
    4. Jim Ridgway, 2016. "Implications of the Data Revolution for Statistics Education," International Statistical Review, International Statistical Institute, vol. 84(3), pages 528-549, 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. Timo Schmid & Markus Zwick, 2022. "Editorial," AStA Wirtschafts- und Sozialstatistisches Archiv, Springer;Deutsche Statistische Gesellschaft - German Statistical Society, vol. 16(3), pages 167-170, December.

    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. Alessandro Casa & Andrea Cappozzo & Michael Fop, 2022. "Group-Wise Shrinkage Estimation in Penalized Model-Based Clustering," Journal of Classification, Springer;The Classification Society, vol. 39(3), pages 648-674, November.
    2. Cimpoeru, Smaranda & Roman, Monica, 2018. "Statistical Literacy and Attitudes Towards Statistics of Romanian Undergraduate Students," MPRA Paper 90452, University Library of Munich, Germany, revised 31 Aug 2018.
    3. Paulo Canas Rodrigues & Elisabetta Carfagna, 2023. "Data science applied to environmental sciences," Environmetrics, John Wiley & Sons, Ltd., vol. 34(1), February.

    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:astaws:v:16:y:2022:i:3:d:10.1007_s11943-022-00311-9. 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.