IDEAS home Printed from https://ideas.repec.org/p/war/wpaper/2026-7.html

Causal Inference under Algorithmic Interference: Identification and Estimation without SUTVA in Platform Economies

Author

Listed:
  • Jakub Ryłow

    (Faculty of Economic Sciences, University of Warsaw)

Abstract

The Stable Unit Treatment Value Assumption (SUTVA) fails systematically in platform economies where a deterministic algorithm mediates all interactions, making interference structural and mechanistically knowable. We introduce algorithmic interference — a structural potential-outcomes model in which spillovers flow through the platform's known decision rule — and construct the Debiased Algorithmic Instrumental Variable (DAIV) estimator: a cross-fitted semiparametric procedure combining Double Machine Learning with the IV equation implied by the algorithmic mechanism. Under local algorithmic monotonicity (LAM), both the ATE and CATE are point identified; without LAM the sharp identified set is characterised. DAIV is sqrt(n)-consistent, asymptotically normal, and semiparametrically efficient, with a formal LAM test supplied. A synthetic ride-sharing example (n = 10,000) shows that standard DML overstates the treatment effect by 52% relative to DAIV; a Hausman-type specification test strongly rejects no algorithmic interference.

Suggested Citation

  • Jakub Ryłow, 2026. "Causal Inference under Algorithmic Interference: Identification and Estimation without SUTVA in Platform Economies," Working Papers 2026-7, Faculty of Economic Sciences, University of Warsaw.
    • Handle: RePEc:war:wpaper:2026-7
    as

    Download full text from publisher

    File URL: https://www.wne.uw.edu.pl/download_file/7130/0
    File Function: First version, 2026
    Download Restriction: no
    ---><---

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;
    ;
    ;
    ;
    ;

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • C26 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Instrumental Variables (IV) Estimation
    • D47 - Microeconomics - - Market Structure, Pricing, and Design - - - Market Design
    • L86 - Industrial Organization - - Industry Studies: Services - - - Information and Internet Services; Computer Software

    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:war:wpaper:2026-7. 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: Jacek Rapacz (email available below). General contact details of provider: https://edirc.repec.org/data/fesuwpl.html .

    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.