IDEAS home Printed from https://ideas.repec.org/a/sae/somere/v42y2013i2p143-163.html
   My bibliography  Save this article

Under What Assumptions Do Site-by-Treatment Instruments Identify Average Causal Effects?

Author

Listed:
  • Sean F. Reardon
  • Stephen W. Raudenbush

Abstract

The increasing availability of data from multisite randomized trials provides a potential opportunity to use instrumental variables (IV) methods to study the effects of multiple hypothesized mediators of the effect of a treatment. We derive nine assumptions needed to identify the effects of multiple mediators when using site-by-treatment interactions to generate multiple instruments. Three of these assumptions are unique to the multiple-site, multiple-mediator case: (1) the assumption that the mediators act in parallel (no mediator affects another mediator); (2) the assumption that the site-average effect of the treatment on each mediator is independent of the site-average effect of each mediator on the outcome; and (3) the assumption that the site-by-compliance matrix has sufficient rank. The first two of these assumptions are nontrivial and cannot be empirically verified, suggesting that multiple-site, multiple-mediator IV models must be justified by strong theory.

Suggested Citation

  • Sean F. Reardon & Stephen W. Raudenbush, 2013. "Under What Assumptions Do Site-by-Treatment Instruments Identify Average Causal Effects?," Sociological Methods & Research, , vol. 42(2), pages 143-163, May.
  • Handle: RePEc:sae:somere:v:42:y:2013:i:2:p:143-163
    DOI: 10.1177/0049124113494575
    as

    Download full text from publisher

    File URL: https://journals.sagepub.com/doi/10.1177/0049124113494575
    Download Restriction: no

    File URL: https://libkey.io/10.1177/0049124113494575?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. Jeffrey R Kling & Jeffrey B Liebman & Lawrence F Katz, 2007. "Experimental Analysis of Neighborhood Effects," Econometrica, Econometric Society, vol. 75(1), pages 83-119, January.
    2. Imbens, Guido W & Angrist, Joshua D, 1994. "Identification and Estimation of Local Average Treatment Effects," Econometrica, Econometric Society, vol. 62(2), pages 467-475, March.
    3. James J. Heckman & Sergio Urzua & Edward Vytlacil, 2006. "Understanding Instrumental Variables in Models with Essential Heterogeneity," The Review of Economics and Statistics, MIT Press, vol. 88(3), pages 389-432, August.
    4. Heckman, James J. & Robb, Richard Jr., 1985. "Alternative methods for evaluating the impact of interventions : An overview," Journal of Econometrics, Elsevier, vol. 30(1-2), pages 239-267.
    Full references (including those not matched with items on IDEAS)

    Citations

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


    Cited by:

    1. Helen Lee & Sarah Shea Crowne & Melanie Estarziau & Keith Kranker & Charles Michalopoulos & Anne Warren & Tod Mijanovich & Jill H. Filene & Anne Duggan & Virginia Knox, "undated". "The Effects of Home Visiting on Prenatal Health, Birth Outcomes, and Health Care Use in the First Year of Life: Final Implementation and Impact Findings from the Mother and Infant Home Visiting Progra," Mathematica Policy Research Reports a9626a8d90bf4f01811d0c9d7, Mathematica Policy Research.
    2. Charles Michalopoulos & Kristen Faucetta & Carolyn J. Hill & Zimena A. Portilla & Lori Burrell & Helen Lee & Anne Duggan & Virginia Knox, "undated". "Impacts on Family Outcomes of Evidence-Based Early Childhood Home Visiting: Results from the Mother and Infant Home Visiting Program Evaluation," Mathematica Policy Research Reports 3adcbd3368c545679a6784b8a, Mathematica Policy Research.
    3. Taylor, Eric, 2014. "Spending more of the school day in math class: Evidence from a regression discontinuity in middle school," Journal of Public Economics, Elsevier, vol. 117(C), pages 162-181.

    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. Guilhem Bascle, 2008. "Controlling for endogeneity with instrumental variables in strategic management research," Post-Print hal-00576795, HAL.
    2. Heckman, James J. & Schmierer, Daniel & Urzua, Sergio, 2010. "Testing the correlated random coefficient model," Journal of Econometrics, Elsevier, vol. 158(2), pages 177-203, October.
    3. Patrick Kline & Christopher R. Walters, 2019. "On Heckits, LATE, and Numerical Equivalence," Econometrica, Econometric Society, vol. 87(2), pages 677-696, March.
    4. Robert A. Moffitt & Matthew V. Zahn, 2019. "The Marginal Labor Supply Disincentives of Welfare: Evidence from Administrative Barriers to Participation," NBER Working Papers 26028, National Bureau of Economic Research, Inc.
    5. Guido W. Imbens & Jeffrey M. Wooldridge, 2009. "Recent Developments in the Econometrics of Program Evaluation," Journal of Economic Literature, American Economic Association, vol. 47(1), pages 5-86, March.
    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. Heckman, James J. & Schmierer, Daniel, 2010. "Tests of hypotheses arising in the correlated random coefficient model," Economic Modelling, Elsevier, vol. 27(6), pages 1355-1367, November.
    8. Lamin Dibba & Manfred Zeller & Aliou Diagne, 2017. "The impact of new Rice for Africa (NERICA) adoption on household food security and health in the Gambia," Food Security: The Science, Sociology and Economics of Food Production and Access to Food, Springer;The International Society for Plant Pathology, vol. 9(5), pages 929-944, October.
    9. Gong, Jie & Lu, Yi & Xie, Huihua, 2020. "The average and distributional effects of teenage adversity on long-term health," Journal of Health Economics, Elsevier, vol. 71(C).
    10. Cornelissen, Thomas & Dustmann, Christian & Raute, Anna & Schönberg, Uta, 2016. "From LATE to MTE: Alternative methods for the evaluation of policy interventions," Labour Economics, Elsevier, vol. 41(C), pages 47-60.
    11. Lina Zhang & David T. Frazier & D. S. Poskitt & Xueyan Zhao, 2020. "Decomposing Identification Gains and Evaluating Instrument Identification Power for Partially Identified Average Treatment Effects," Papers 2009.02642, arXiv.org, revised Sep 2022.
    12. James Berry & Greg Fischer & Raymond Guiteras, 2020. "Eliciting and Utilizing Willingness to Pay: Evidence from Field Trials in Northern Ghana," Journal of Political Economy, University of Chicago Press, vol. 128(4), pages 1436-1473.
    13. James J. Heckman, 2008. "The Principles Underlying Evaluation Estimators with an Application to Matching," Annals of Economics and Statistics, GENES, issue 91-92, pages 9-73.
    14. Rojas, Eugenio & Sánchez, Rafael & Villena, Mauricio G., 2016. "Credit constraints in higher education in a context of unobserved heterogeneity," Economics of Education Review, Elsevier, vol. 52(C), pages 225-250.
    15. Arthur Lewbel, 2019. "The Identification Zoo: Meanings of Identification in Econometrics," Journal of Economic Literature, American Economic Association, vol. 57(4), pages 835-903, December.
    16. Baum-Snow, Nathaniel & Ferreira, Fernando, 2015. "Causal Inference in Urban and Regional Economics," Handbook of Regional and Urban Economics, in: Gilles Duranton & J. V. Henderson & William C. Strange (ed.), Handbook of Regional and Urban Economics, edition 1, volume 5, chapter 0, pages 3-68, Elsevier.
    17. Patrick Kline & Christopher R. Walters, 2016. "Evaluating Public Programs with Close Substitutes: The Case of HeadStart," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 131(4), pages 1795-1848.
    18. Caliendo, Marco & Künn, Steffen & Mahlstedt, Robert, 2017. "The return to labor market mobility: An evaluation of relocation assistance for the unemployed," Journal of Public Economics, Elsevier, vol. 148(C), pages 136-151.
    19. Heckman, James J., 2010. "The Assumptions Underlying Evaluation Estimators," Brazilian Review of Econometrics, Sociedade Brasileira de Econometria - SBE, vol. 30(2), December.
    20. Jeffrey Smith, 2000. "A Critical Survey of Empirical Methods for Evaluating Active Labor Market Policies," Swiss Journal of Economics and Statistics (SJES), Swiss Society of Economics and Statistics (SSES), vol. 136(III), pages 247-268, September.

    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:sae:somere:v:42:y:2013:i:2:p:143-163. 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: SAGE Publications (email available below). General contact details of provider: .

    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.