IDEAS home Printed from https://ideas.repec.org/a/eee/ecolet/v255y2025ics0165176525003180.html

Directed acyclic graph representation of the demand–Supply model

Author

Listed:
  • Carriero, Andrea

Abstract

This note shows how the traditional demand–supply model can be represented using a Directed Acyclic Graph. It explores the relationship between econometrics terminology and causal inference terminology, illustrating that the two frameworks are entirely consistent with each other.

Suggested Citation

  • Carriero, Andrea, 2025. "Directed acyclic graph representation of the demand–Supply model," Economics Letters, Elsevier, vol. 255(C).
    • Handle: RePEc:eee:ecolet:v:255:y:2025:i:c:s0165176525003180
    DOI: 10.1016/j.econlet.2025.112481
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0165176525003180
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.econlet.2025.112481?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

    for a different version of it.

    References listed on IDEAS

    as
    1. Guido W. Imbens, 2020. "Potential Outcome and Directed Acyclic Graph Approaches to Causality: Relevance for Empirical Practice in Economics," Journal of Economic Literature, American Economic Association, vol. 58(4), pages 1129-1179, December.
    2. Nick Huntington-Klein, 2022. "Pearl before economists: the book of why and empirical economics," Journal of Economic Methodology, Taylor & Francis Journals, vol. 29(4), pages 326-334, October.
    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. Guido W. Imbens, 2022. "Causality in Econometrics: Choice vs Chance," Econometrica, Econometric Society, vol. 90(6), pages 2541-2566, November.
    2. Christoph Breunig & Patrick Burauel, 2021. "Testability of Reverse Causality Without Exogenous Variation," Papers 2107.05936, arXiv.org, revised Apr 2024.
    3. De Borger, Bruno & Mulalic, Ismir & Rouwendal, Jan, 2025. "Productivity and wage effects of an exogenous improvement in transport infrastructure: Accessibility and the Great Belt Bridge," Regional Science and Urban Economics, Elsevier, vol. 114(C).
    4. Bingbo Gao & Jianyu Yang & Ziyue Chen & George Sugihara & Manchun Li & Alfred Stein & Mei-Po Kwan & Jinfeng Wang, 2023. "Causal inference from cross-sectional earth system data with geographical convergent cross mapping," Nature Communications, Nature, vol. 14(1), pages 1-12, December.
    5. 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.
    6. Peter Hull & Michal Kolesár & Christopher Walters, 2022. "Labor by design: contributions of David Card, Joshua Angrist, and Guido Imbens," Scandinavian Journal of Economics, Wiley Blackwell, vol. 124(3), pages 603-645, July.
    7. Vikram Dayal & Anand Murugesan, 2020. "Demystifying causal inference: ingredients of a recipe," IEG Working Papers 393, Institute of Economic Growth.
    8. Barnor, Kodjo & Caton, James & Miljkovic, Dragan, 2023. "The role of funding on research and science: The impact of glyphosate herbicides on health and the environment," Journal of Policy Modeling, Elsevier, vol. 45(1), pages 103-120.
    9. Shishir Shakya & Nabamita Dutta, 2024. "How Individualism Influences Female Financial Inclusion through Education: Evidence from Historical Prevalence of Infectious Diseases," Working Papers 24-07, Department of Economics, Appalachian State University.
    10. Kulkarni, Nirupama & Malmendier, Ulrike, 2022. "Homeownership segregation," Journal of Monetary Economics, Elsevier, vol. 129(C), pages 123-149.
    11. Heiss, Andrew, 2025. "Statistical methods in public policy research," SocArXiv cwymb_v1, Center for Open Science.
    12. Neil Christy & Amanda Ellen Kowalski, 2024. "Counting Defiers: A Design-Based Model of an Experiment Can Reveal Evidence Beyond the Average Effect," Papers 2412.16352, arXiv.org, revised Mar 2026.
    13. Alex Coad, 2021. "Econometrics and the Growth of Firms: Perspectives from Evolutionary Economics," Strategy Science, INFORMS, vol. 6(4), pages 338-352, December.
    14. Martin Huber, 2024. "An introduction to causal discovery," Swiss Journal of Economics and Statistics, Springer;Swiss Society of Economics and Statistics, vol. 160(1), pages 1-16, December.
    15. Chen, Lifeng & Wang, Kaifeng, 2022. "The spatial spillover effect of low-carbon city pilot scheme on green efficiency in China's cities: Evidence from a quasi-natural experiment," Energy Economics, Elsevier, vol. 110(C).
    16. Philipp Baumann & Enzo Rossi & Michael Schomaker, 2022. "Estimating the effect of central bank independence on inflation using longitudinal targeted maximum likelihood estimation," IFC Bulletins chapters, in: Bank for International Settlements (ed.), Machine learning in central banking, volume 57, Bank for International Settlements.
    17. Öberg, Stefan, 2021. "Treatment for natural experiments: How to improve causal estimates using conceptual definitions and substantive interpretations," SocArXiv pkyue, Center for Open Science.
    18. Arne Henningsen & Guy Low & David Wuepper & Tobias Dalhaus & Hugo Storm & Dagim Belay & Stefan Hirsch, 2024. "Estimating Causal Effects with Observational Data: Guidelines for Agricultural and Applied Economists," IFRO Working Paper 2024/03, University of Copenhagen, Department of Food and Resource Economics.
    19. Dragan Miljkovic & Cole Goetz, 2023. "Futures markets and price stabilisation: An analysis of soybeans markets in North America," Australian Journal of Agricultural and Resource Economics, Australian Agricultural and Resource Economics Society, vol. 67(1), pages 104-117, January.
    20. Su, Zhi & Liu, Peng & Fang, Tong, 2022. "Uncertainty matters in US financial information spillovers: Evidence from a directed acyclic graph approach," The Quarterly Review of Economics and Finance, Elsevier, vol. 84(C), pages 229-242.

    More about this item

    Keywords

    ;
    ;
    ;

    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:eee:ecolet:v:255:y:2025:i:c:s0165176525003180. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/ecolet .

    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.