IDEAS home Printed from https://ideas.repec.org/p/cpr/ceprdp/20400.html

On Causal Inference with Model-Based Outcomes

Author

Listed:
  • Arkhangelsky, Dmitry
  • Yanagimoto, Kazuharu
  • Zohar, Tom

Abstract

We study a causal inference problem with group-level outcomes, which are themselves parameters identified from microdata. We formalize these outcomes using population moment conditions and demonstrate that one-step Generalized Method of Moments (GMM) estimators are generally inconsistent due to an endogenous weighting bias, where policy affects the implicit GMM weights. In contrast, two-stage Minimum Distance (MD) estimators perform well when group sizes are sufficiently large. While MD estimators can still be inconsistent in small groups due to a policy-induced sample selection, we demonstrate that this can be addressed by incorporating auxiliary population information. An empirical application illustrates the practical importance of these findings.

Suggested Citation

  • Arkhangelsky, Dmitry & Yanagimoto, Kazuharu & Zohar, Tom, 2025. "On Causal Inference with Model-Based Outcomes," CEPR Discussion Papers 20400, C.E.P.R. Discussion Papers.
    • Handle: RePEc:cpr:ceprdp:20400
    as

    Download full text from publisher

    File URL: https://cepr.org/publications/DP20400
    Download Restriction: no
    ---><---

    More about this item

    JEL classification:

    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • J13 - Labor and Demographic Economics - - Demographic Economics - - - Fertility; Family Planning; Child Care; Children; Youth
    • J31 - Labor and Demographic Economics - - Wages, Compensation, and Labor Costs - - - Wage Level and Structure; Wage Differentials
    • D24 - Microeconomics - - Production and Organizations - - - Production; Cost; Capital; Capital, Total Factor, and Multifactor Productivity; Capacity

    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:cpr:ceprdp:20400. 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: CEPR (email available below). General contact details of provider: https://cepr.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.