IDEAS home Printed from https://ideas.repec.org/p/inu/caeprp/2008008.html
   My bibliography  Save this paper

Minimizing Bias in Selection on Observables Estimators When Unconfoundness Fails

Author

Listed:
  • Daniel Millimet

    (Southern Methodist University)

  • Rusty Tchernis

    (Indiana University Bloomington)

Abstract

We characterize the bias of propensity score based estimators of common average treatment effect parameters in the case of selection on unobservables. We then propose a new minimum biased estimator of the average treatment effect. We assess the finite sample performance of our estimator using simulated data, as well as a timely application examining the causal effect of the School Breakfast Program on childhood obesity. We find our new estimator to be quite advantageous in many situations, even when selection is only on observables.

Suggested Citation

  • Daniel Millimet & Rusty Tchernis, 2008. "Minimizing Bias in Selection on Observables Estimators When Unconfoundness Fails," CAEPR Working Papers 2008-008, Center for Applied Economics and Policy Research, Department of Economics, Indiana University Bloomington.
    • Handle: RePEc:inu:caeprp:2008008
    as

    Download full text from publisher

    File URL: https://caepr.indiana.edu/RePEc/inu/caeprp/caepr2008-008.pdf
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. James J. Heckman & Edward Vytlacil, 2005. "Structural Equations, Treatment Effects, and Econometric Policy Evaluation," Econometrica, Econometric Society, vol. 73(3), pages 669-738, May.
    2. Daniel L. Millimet & Rusty Tchernis & Muna Husain, 2010. "School Nutrition Programs and the Incidence of Childhood Obesity," Journal of Human Resources, University of Wisconsin Press, vol. 45(3).
    3. Keisuke Hirano & Guido W. Imbens & Geert Ridder, 2003. "Efficient Estimation of Average Treatment Effects Using the Estimated Propensity Score," Econometrica, Econometric Society, vol. 71(4), pages 1161-1189, July.
    4. Jayanta Bhattacharya & Janet Currie & Steven J. Haider, 2006. "Breakfast of Champions?: The School Breakfast Program and the Nutrition of Children and Families," Journal of Human Resources, University of Wisconsin Press, vol. 41(3).
    5. Millimet, Daniel L. & Tchernis, Rusty, 2009. "On the Specification of Propensity Scores, With Applications to the Analysis of Trade Policies," Journal of Business & Economic Statistics, American Statistical Association, vol. 27(3), pages 397-415.
    6. A. Smith, Jeffrey & E. Todd, Petra, 2005. "Does matching overcome LaLonde's critique of nonexperimental estimators?," Journal of Econometrics, Elsevier, vol. 125(1-2), pages 305-353.
    7. Odelia Rosin, 2008. "The Economic Causes Of Obesity: A Survey," Journal of Economic Surveys, Wiley Blackwell, vol. 22(4), pages 617-647, September.
    8. Black, Dan A. & Smith, J.A.Jeffrey A., 2004. "How robust is the evidence on the effects of college quality? Evidence from matching," Journal of Econometrics, Elsevier, vol. 121(1-2), pages 99-124.
    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. Owusu, Victor & Abdulai, Awudu & Abdul-Rahman, Seini, 2011. "Non-farm work and food security among farm households in Northern Ghana," Food Policy, Elsevier, vol. 36(2), pages 108-118, April.
    2. Gurun, Ayfer & Millimet, Daniel L., 2008. "Does Private Tutoring Payoff?," IZA Discussion Papers 3637, Institute of Labor Economics (IZA).
    3. Zhang, Chunqin & Juan, Zhicai & Xiao, Guangnian, 2015. "Do contractual practices affect technical efficiency? Evidence from public transport operators in China," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 80(C), pages 39-55.

    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. Daniel L. Millimet & Rusty Tchernis, 2013. "Estimation Of Treatment Effects Without An Exclusion Restriction: With An Application To The Analysis Of The School Breakfast Program," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 28(6), pages 982-1017, September.
    2. Millimet, Daniel L. & Tchernis, Rusty, 2008. "Minimizing Bias in Selection on Observables Estimators When Unconfoundness Fails," IZA Discussion Papers 3632, Institute for the Study of Labor (IZA).
    3. Daniel L. Millimet & Rusty Tchernis & Muna Husain, 2010. "School Nutrition Programs and the Incidence of Childhood Obesity," Journal of Human Resources, University of Wisconsin Press, vol. 45(3).
    4. Chunrong Ai & Oliver Linton & Kaiji Motegi & Zheng Zhang, 2021. "A unified framework for efficient estimation of general treatment models," Quantitative Economics, Econometric Society, vol. 12(3), pages 779-816, July.
    5. Bernhard Schmidpeter, 2015. "The Fatal Consequences of Grief," CDL Aging, Health, Labor working papers 2015-07, The Christian Doppler (CD) Laboratory Aging, Health, and the Labor Market, Johannes Kepler University Linz, Austria.
    6. Carlos A. Flores & Oscar A. Mitnik, 2009. "Evaluating Nonexperimental Estimators for Multiple Treatments: Evidence from Experimental Data," Working Papers 2010-10, University of Miami, Department of Economics.
    7. 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.
    8. Marco Caliendo & Sabine Kopeinig, 2008. "Some Practical Guidance For The Implementation Of Propensity Score Matching," Journal of Economic Surveys, Wiley Blackwell, vol. 22(1), pages 31-72, February.
    9. Jeffrey Smith & Arthur Sweetman, 2016. "Viewpoint: Estimating the causal effects of policies and programs," Canadian Journal of Economics, Canadian Economics Association, vol. 49(3), pages 871-905, August.
    10. Chad D. Meyerhoefer & Muzhe Yang, 2011. "The Relationship between Food Assistance and Health: A Review of the Literature and Empirical Strategies for Identifying Program Effects," Applied Economic Perspectives and Policy, Agricultural and Applied Economics Association, vol. 33(3), pages 304-344.
    11. Peter R. Mueser & Kenneth R. Troske & Alexey Gorislavsky, 2007. "Using State Administrative Data to Measure Program Performance," The Review of Economics and Statistics, MIT Press, vol. 89(4), pages 761-783, November.
    12. List John A. & Millimet Daniel L, 2008. "The Market: Catalyst for Rationality and Filter of Irrationality," The B.E. Journal of Economic Analysis & Policy, De Gruyter, vol. 8(1), pages 1-55, November.
    13. Gustavo Canavire-Bacarreza & Luis Castro Peñarrieta & Darwin Ugarte Ontiveros, 2021. "Outliers in Semi-Parametric Estimation of Treatment Effects," Econometrics, MDPI, vol. 9(2), pages 1-32, April.
    14. Lorenzo Almada & Ian McCarthy & Rusty Tchernis, 2016. "What Can We Learn about the Effects of Food Stamps on Obesity in the Presence of Misreporting?," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 98(4), pages 997-1017.
    15. W. Bentley MacLeod, 2017. "Viewpoint: The human capital approach to inference," Canadian Journal of Economics/Revue canadienne d'économique, John Wiley & Sons, vol. 50(1), pages 5-39, February.
    16. Ai, Chunrong & Linton, Oliver & Zhang, Zheng, 2022. "Estimation and inference for the counterfactual distribution and quantile functions in continuous treatment models," Journal of Econometrics, Elsevier, vol. 228(1), pages 39-61.
    17. Steven Lehrer & Gregory Kordas, 2013. "Matching using semiparametric propensity scores," Empirical Economics, Springer, vol. 44(1), pages 13-45, February.
    18. Sokbae Lee & Yoon-Jae Whang, 2009. "Nonparametric Tests of Conditional Treatment Effects," Cowles Foundation Discussion Papers 1740, Cowles Foundation for Research in Economics, Yale University.
    19. Shanwen Zhu & Man Li & Renyao Zhong & Peter C. Coyte, 2019. "The Effects of Co-Residence on the Subjective Well-Being of Older Chinese Parents," Sustainability, MDPI, vol. 11(7), pages 1-17, April.
    20. Zhao, Zhong, 2008. "Sensitivity of propensity score methods to the specifications," Economics Letters, Elsevier, vol. 98(3), pages 309-319, March.

    More about this item

    Keywords

    Treatment Effects; Propensity Score; Bias; Unconfoundedness; Selection on Unobservables;
    All these keywords.

    JEL classification:

    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection

    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:inu:caeprp:2008008. 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: Center for Applied Economics and Policy Research (email available below). General contact details of provider: https://edirc.repec.org/data/caeprus.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.