IDEAS home Printed from https://ideas.repec.org/p/fip/fedcwp/1505.html
   My bibliography  Save this paper

A distinction between causal effects in structural and rubin causal models

Author

Listed:
  • Dionissi Aliprantis

Abstract

Structural Causal Models define causal effects in terms of a single Data Generating Process (DGP), and the Rubin Causal Model defines causal effects in terms of a model that can represent counterfactuals from many DGPs. Under these different definitions, notationally similar causal effects make distinct claims about the results of interventions to the system under investigation: Structural equations imply conditional independencies in the data that potential outcomes do not. One implication is that the DAG of a Rubin Causal Model is different from the DAG of a Structural Causal Model. Another is that Pearl?s do-calculus does not apply to potential outcomes and the Rubin Causal Model.

Suggested Citation

  • Dionissi Aliprantis, 2015. "A distinction between causal effects in structural and rubin causal models," Working Papers (Old Series) 1505, Federal Reserve Bank of Cleveland.
    • Handle: RePEc:fip:fedcwp:1505
    DOI: 10.26509/frbc-wp-201505
    as

    Download full text from publisher

    File URL: https://doi.org/10.26509/frbc-wp-201505
    File Function: Persistent link
    Download Restriction: no
    File URL: https://www.clevelandfed.org/-/media/project/clevelandfedtenant/clevelandfedsite/publications/working-papers/2015/wp-1505-a-distinction-between-casual-effects-pdf.pdf
    File Function: Full text
    Download Restriction: no
    File URL: https://libkey.io/10.26509/frbc-wp-201505?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
    ---><---

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Dionissi Aliprantis, 2011. "Assessing the evidence on neighborhood effects from moving to opportunity," Working Papers (Old Series) 1101, Federal Reserve Bank of Cleveland.
    2. Dionissi Aliprantis, 2017. "Human capital in the inner city," Empirical Economics, Springer, vol. 53(3), pages 1125-1169, November.
    3. Dionissi Aliprantis, 2017. "Assessing the evidence on neighborhood effects from Moving to Opportunity," Empirical Economics, Springer, vol. 52(3), pages 925-954, May.

    More about this item

    Keywords

    Structural Equation; Potential Outcome; Invariance; Autonomy;
    All these keywords.

    JEL classification:

    • C00 - Mathematical and Quantitative Methods - - General - - - General
    • C01 - Mathematical and Quantitative Methods - - General - - - Econometrics
    • C31 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions; Social Interaction Models

    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:fip:fedcwp:1505. 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: 4D Library (email available below). General contact details of provider: https://edirc.repec.org/data/frbclus.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.