IDEAS home Printed from https://ideas.repec.org/p/zbw/wzblpe/spi2004101.html
   My bibliography  Save this paper

Assessing bias in the estimation of causal effects: Rosenbaum bounds on matching estimators and instrumental variables estimation with imperfect instruments

Author

Listed:
  • DiPrete, Thomas A.
  • Gangl, Markus

Abstract

Propensity score matching provides an estimate of the effect of a 'treatment' variable on an outcome variable that is largely free of bias arising from an association between treatment status and observable variables. However, matching methods are not robust against 'hidden bias' arising from unobserved variables that simultaneously affect assignment to treatment and the outcome variable. One strategy for addressing this problem is the Rosenbaum bounds approach, which allows the analyst to determine how strongly an unmeasured confounding variable must affect selection into treatment in order to undermine the conclusions about causal effects from a matching analysis. Instrumental variables (IV) estimation provides an alternative strategy for the estimation of causal effects, but the method typically reduces the precision of the estimate and has an additional source of uncertainty that derives from the untestable nature of the assumptions of the IV approach. A method of assessing this additional uncertainty is proposed so that the total uncertainty of the IV approach can be compared with the Rosenbaum bounds approach to uncertainty using matching methods. Because the approaches rely on different information and different assumptions, they provide complementary information about causal relationships. The approach is illustrated via an analysis of the impact of unemployment insurance on the timing of reemployment, the postunemployment wage, and the probability of relocation, using data from several panels of the Survey of Income and Program Participation (SIPP).

Suggested Citation

  • DiPrete, Thomas A. & Gangl, Markus, 2004. "Assessing bias in the estimation of causal effects: Rosenbaum bounds on matching estimators and instrumental variables estimation with imperfect instruments," Discussion Papers, Research Unit: Labor Market Policy and Employment SP I 2004-101, WZB Berlin Social Science Center.
  • Handle: RePEc:zbw:wzblpe:spi2004101
    as

    Download full text from publisher

    File URL: https://www.econstor.eu/bitstream/10419/44000/1/385729642.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Michael E. Sobel, 1996. "An Introduction to Causal Inference," Sociological Methods & Research, , vol. 24(3), pages 353-379, February.
    2. Joshua D. Angrist, 2004. "Treatment effect heterogeneity in theory and practice," Economic Journal, Royal Economic Society, vol. 114(494), pages 52-83, March.
    3. 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.
    4. Michael Lechner, 1999. "Nonparametric bounds on employment and income effects of continuous vocational training in East Germany," Econometrics Journal, Royal Economic Society, vol. 2(1), pages 1-28.
    5. James Heckman, 1997. "Instrumental Variables: A Study of Implicit Behavioral Assumptions Used in Making Program Evaluations," Journal of Human Resources, University of Wisconsin Press, vol. 32(3), pages 441-462.
    6. Robert A. Moffitt, 1996. "Selection Bias Adjustment in Treatment-Effect Models as a Method of Aggregation," NBER Technical Working Papers 0187, National Bureau of Economic Research, Inc.
    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. Guilhem Bascle, 2008. "Controlling for endogeneity with instrumental variables in strategic management research," Post-Print hal-00576795, HAL.
    2. Huber Martin & Wüthrich Kaspar, 2019. "Local Average and Quantile Treatment Effects Under Endogeneity: A Review," Journal of Econometric Methods, De Gruyter, vol. 8(1), pages 1-27, January.
    3. Breen, Richard & Ermisch, John, 2021. "Instrumental Variable Estimation in Demographic Studies: The LATE interpretation of the IV estimator with heterogenous effects," SocArXiv vx9m7, Center for Open Science.
    4. Zamarro, Gema, 2010. "Accounting for heterogeneous returns in sequential schooling decisions," Journal of Econometrics, Elsevier, vol. 156(2), pages 260-276, June.
    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. Affuso, Antonio, 2011. "A propensity score analysis of public incentives: The Italian case," MPRA Paper 36698, University Library of Munich, Germany.
    7. repec:diw:diwwpp:dp401 is not listed on IDEAS
    8. Cockx, Bart & Bardoulat, Isabelle, 1999. "Vocational Training: Does it speed up the Transition Rate out of Unemployment ?," LIDAM Discussion Papers IRES 1999032, Université catholique de Louvain, Institut de Recherches Economiques et Sociales (IRES).
    9. Kuckulenz Anja & Maier Michael, 2006. "Heterogeneous Returns to Training: An Analysis with German Data Using Local Instrumental Variables," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 226(1), pages 24-40, February.
    10. Ermisch, John & Pevalin, David J., 2004. "Early childbearing and housing choices," Journal of Housing Economics, Elsevier, vol. 13(3), pages 170-194, September.
    11. Domenico Depalo & Jay Bhattacharya & Vincenzo Atella & Federico Belotti, 2019. "When Technological Advance Meets Physician Learning in Drug Prescribing," NBER Working Papers 26202, National Bureau of Economic Research, Inc.
    12. A. Affuso, 2007. "Credit rationing and real assets: evidence from Italian panel data," Economics Department Working Papers 2007-EP09, Department of Economics, Parma University (Italy).
    13. Huber, Martin & Wüthrich, Kaspar, 2017. "Evaluating local average and quantile treatment effects under endogeneity based on instruments: a review," FSES Working Papers 479, Faculty of Economics and Social Sciences, University of Freiburg/Fribourg Switzerland.
    14. Committee, Nobel Prize, 2021. "Answering causal questions using observational data," Nobel Prize in Economics documents 2021-2, Nobel Prize Committee.
    15. Juanna Schrøter Joensen & Helena Skyt Nielsen, 2009. "Is there a Causal Effect of High School Math on Labor Market Outcomes?," Journal of Human Resources, University of Wisconsin Press, vol. 44(1).
    16. Philip Oreopoulos, 2006. "Estimating Average and Local Average Treatment Effects of Education when Compulsory Schooling Laws Really Matter," American Economic Review, American Economic Association, vol. 96(1), pages 152-175, March.
    17. 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.
    18. Frölich, Markus & Lechner, Michael, 2010. "Exploiting Regional Treatment Intensity for the Evaluation of Labor Market Policies," Journal of the American Statistical Association, American Statistical Association, vol. 105(491), pages 1014-1029.
    19. Stefan Boes, 2013. "Nonparametric analysis of treatment effects in ordered response models," Empirical Economics, Springer, vol. 44(1), pages 81-109, February.
    20. Guillermo Cruces & Sebastian Galiani, 2003. "Generalizing the Causal Effect of Fertility on Female Labor Supply," Labor and Demography 0310002, University Library of Munich, Germany.
    21. Xintong Wang & Carlos A. Flores & Alfonso Flores-Lagunes, 2020. "The Effects of Vietnam-Era Military Service on the Long-Term Health of Veterans: A Bounds Analysis," Center for Policy Research Working Papers 234, Center for Policy Research, Maxwell School, Syracuse University.

    More about this item

    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:zbw:wzblpe:spi2004101. 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: ZBW - Leibniz Information Centre for Economics (email available below). General contact details of provider: https://edirc.repec.org/data/wzbbbde.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.