IDEAS home Printed from https://ideas.repec.org/p/osf/socarx/4bw9e.html
   My bibliography  Save this paper

Introduction to causality in science studies

Author

Listed:
  • Klebel, Thomas
  • Traag, Vincent

Abstract

Sound causal inference is crucial for advancing the study of science. Incorrectly interpreting predictive effects as causal might be ineffective or even detrimental to policy recommendations. Many publications in science studies lack appropriate methods to substantiate their causal claims. We here provide an introduction to structural causal models. Such models, usually represented in a graphical form, allow researchers to make their causal assumptions transparent and provide a foundation for causal inference. We illustrate how to use structural causal models to conduct causal inference using regression models based on simulated data of a hypothetical structural causal model of Open Science. The graphical representation of structural causal models allows researchers to clearly communicate their assumptions and findings, thereby fostering further discussion. We hope our introduction helps more researchers in science studies to consider causality explicitly.

Suggested Citation

  • Klebel, Thomas & Traag, Vincent, 2024. "Introduction to causality in science studies," SocArXiv 4bw9e, Center for Open Science.
  • Handle: RePEc:osf:socarx:4bw9e
    DOI: 10.31219/osf.io/4bw9e
    as

    Download full text from publisher

    File URL: https://osf.io/download/65c6006c35be200d69a50133/
    Download Restriction: no

    File URL: https://libkey.io/10.31219/osf.io/4bw9e?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
    ---><---

    References listed on IDEAS

    as
    1. R. Dean Malmgren & Julio M. Ottino & Luís A. Nunes Amaral, 2010. "The role of mentorship in protégé performance," Nature, Nature, vol. 465(7298), pages 622-626, June.
    2. Kwon, Seokbeom & Motohashi, Kazuyuki, 2021. "Incentive or disincentive for research data disclosure? A large-scale empirical analysis and implications for open science policy," International Journal of Information Management, Elsevier, vol. 60(C).
    3. Cassidy R. Sugimoto & Chaoqun Ni & Terrell G. Russell & Brenna Bychowski, 2011. "Academic genealogy as an indicator of interdisciplinarity: An examination of dissertation networks in Library and Information Science," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 62(9), pages 1808-1828, September.
    4. Marcus R. Munafò & George Davey Smith, 2018. "Robust research needs many lines of evidence," Nature, Nature, vol. 553(7689), pages 399-401, January.
    5. Cassidy R. Sugimoto & Chaoqun Ni & Terrell G. Russell & Brenna Bychowski, 2011. "Academic genealogy as an indicator of interdisciplinarity: An examination of dissertation networks in Library and Information Science," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 62(9), pages 1808-1828, September.
    6. Heather A Piwowar & Roger S Day & Douglas B Fridsma, 2007. "Sharing Detailed Research Data Is Associated with Increased Citation Rate," PLOS ONE, Public Library of Science, vol. 2(3), pages 1-5, March.
    7. Lu Liu & Benjamin F. Jones & Brian Uzzi & Dashun Wang, 2023. "Data, measurement and empirical methods in the science of science," Nature Human Behaviour, Nature, vol. 7(7), pages 1046-1058, July.
    8. Paul Hunermund & Elias Bareinboim, 2019. "Causal Inference and Data Fusion in Econometrics," Papers 1912.09104, arXiv.org, revised Mar 2023.
    Full references (including those not matched with items on IDEAS)

    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. Wuestman, Mignon & Wanzenböck, Iris & Frenken, Koen, 2023. "Local peer communities and future academic success of Ph.D. candidates," Research Policy, Elsevier, vol. 52(8).
    2. Timothy D. Bowman & Andrew Tsou & Chaoqun Ni & Cassidy R. Sugimoto, 2014. "Post-interdisciplinary frames of reference: exploring permeability and perceptions of disciplinarity in the social sciences," Scientometrics, Springer;Akadémiai Kiadó, vol. 101(3), pages 1695-1714, December.
    3. Jianhua Hou & Bili Zheng & Yang Zhang & Chaomei Chen, 2021. "How do Price medalists’ scholarly impact change before and after their awards?," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(7), pages 5945-5981, July.
    4. Rafael J. P. Damaceno & Luciano Rossi & Rogério Mugnaini & Jesús P. Mena-Chalco, 2019. "The Brazilian academic genealogy: evidence of advisor–advisee relationships through quantitative analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 119(1), pages 303-333, April.
    5. Debarshi Kumar Sanyal & Sumana Dey & Partha Pratim Das, 2020. "gm-index: a new mentorship index for researchers," Scientometrics, Springer;Akadémiai Kiadó, vol. 123(1), pages 71-102, April.
    6. Rossi, Luciano & Damaceno, Rafael J.P. & Freire, Igor L. & Bechara, Etelvino J.H. & Mena-Chalco, Jesús P., 2018. "Topological metrics in academic genealogy graphs," Journal of Informetrics, Elsevier, vol. 12(4), pages 1042-1058.
    7. Pertti Vakkari & Yu-Wei Chang & Kalervo Järvelin, 2022. "Largest contribution to LIS by external disciplines as measured by the characteristics of research articles," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(8), pages 4499-4522, August.
    8. Shiji Chen & Clément Arsenault & Yves Gingras & Vincent Larivière, 2015. "Exploring the interdisciplinary evolution of a discipline: the case of Biochemistry and Molecular Biology," Scientometrics, Springer;Akadémiai Kiadó, vol. 102(2), pages 1307-1323, February.
    9. Meijun Liu & Sijie Yang & Yi Bu & Ning Zhang, 2023. "Female early-career scientists have conducted less interdisciplinary research in the past six decades: evidence from doctoral theses," Palgrave Communications, Palgrave Macmillan, vol. 10(1), pages 1-16, December.
    10. Mignon Wuestman & Koen Frenken & Iris Wanzenböck, 2020. "A genealogical approach to academic success," PLOS ONE, Public Library of Science, vol. 15(12), pages 1-16, December.
    11. Rossi, Luciano & Freire, Igor L. & Mena-Chalco, Jesús P., 2017. "Genealogical index: A metric to analyze advisor–advisee relationships," Journal of Informetrics, Elsevier, vol. 11(2), pages 564-582.
    12. Cristóbal Urbano & Jordi Ardanuy, 2020. "Cross-disciplinary collaboration versus coexistence in LIS serials: analysis of authorship affiliations in four European countries," Scientometrics, Springer;Akadémiai Kiadó, vol. 124(1), pages 575-602, July.
    13. Steven Cooke & Jesse Vermaire, 2015. "Environmental studies and environmental science today: inevitable mission creep and integration in action-oriented transdisciplinary areas of inquiry, training and practice," Journal of Environmental Studies and Sciences, Springer;Association of Environmental Studies and Sciences, vol. 5(1), pages 70-78, March.
    14. Fu, Zhongmeng & Cao, Yuan & Zhao, Yong, 2024. "Identifying knowledge evolution in computer science from the perspective of academic genealogy," Journal of Informetrics, Elsevier, vol. 18(2).
    15. Lu, Wei & Ren, Yan & Huang, Yong & Bu, Yi & Zhang, Yuehan, 2021. "Scientific collaboration and career stages: An ego-centric perspective," Journal of Informetrics, Elsevier, vol. 15(4).
    16. Benedikt Fecher & Sascha Friesike & Marcel Hebing, 2014. "What Drives Academic Data Sharing?," SOEPpapers on Multidisciplinary Panel Data Research 655, DIW Berlin, The German Socio-Economic Panel (SOEP).
    17. Andreoli-Versbach, Patrick & Mueller-Langer, Frank, 2014. "Open access to data: An ideal professed but not practised," Research Policy, Elsevier, vol. 43(9), pages 1621-1633.
    18. Chaocheng He & Jiang Wu & Qingpeng Zhang, 2022. "Proximity‐aware research leadership recommendation in research collaboration via deep neural networks," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 73(1), pages 70-89, January.
    19. Simon Calmar Andersen & Louise Beuchert & Phillip Heiler & Helena Skyt Nielsen, 2023. "A Guide to Impact Evaluation under Sample Selection and Missing Data: Teacher's Aides and Adolescent Mental Health," Papers 2308.04963, arXiv.org.
    20. Rik Peels & Lex Bouter, 2018. "The possibility and desirability of replication in the humanities," Palgrave Communications, Palgrave Macmillan, vol. 4(1), pages 1-4, December.

    More about this item

    NEP fields

    This paper has been announced in the following NEP Reports:

    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:osf:socarx:4bw9e. 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: OSF (email available below). General contact details of provider: https://arabixiv.org .

    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.