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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
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    File URL: https://caepr.indiana.edu/RePEc/inu/caeprp/caepr2008-008.pdf
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    References listed on IDEAS

    as
    1. 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).
    2. Odelia Rosin, 2008. "The Economic Causes Of Obesity: A Survey," Journal of Economic Surveys, Wiley Blackwell, vol. 22(4), pages 617-647, September.
    3. 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).
    4. 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.
    5. 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.
    6. James J. Heckman & Edward Vytlacil, 2005. "Structural Equations, Treatment Effects, and Econometric Policy Evaluation," Econometrica, Econometric Society, vol. 73(3), pages 669-738, May.
    7. 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.
    8. 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.
    Full references (including those not matched with items on IDEAS)

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    Cited by:

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    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.

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    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

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