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Minimizing Bias in Selection on Observables Estimators When Unconfoundness Fails

  • Millimet, Daniel L.


    (Southern Methodist University)

  • Tchernis, Rusty


    (Georgia State University)

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.

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Paper provided by Institute for the Study of Labor (IZA) in its series IZA Discussion Papers with number 3632.

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Length: 39 pages
Date of creation: Aug 2008
Date of revision:
Publication status: published as 'Estimation of Treatment Effects Without an Exclusion Restriction: with an Application to the Analysis of the School Breakfast Program' in: Journal of Applied Econometrics, 2013, 28 (6), 982–1017
Handle: RePEc:iza:izadps:dp3632
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  1. Millimet, Daniel L. & Tchernis, Rusty & Husain, Muna, 2008. "School Nutrition Programs and the Incidence of Childhood Obesity," IZA Discussion Papers 3664, Institute for the Study of Labor (IZA).
  2. 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.
  3. Guido Imbens, 2000. "Efficient Estimation of Average Treatment Effects Using the Estimated Propensity Score," Econometric Society World Congress 2000 Contributed Papers 1166, Econometric Society.
  4. Jay Bhattacharya & Janet Currie & Steven Haider, 2004. "Breakfast of Champions? The School Breakfast Program and the Nutrition of Children and Families," Working Papers 189, RAND Corporation Publications Department.
  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," NBER Working Papers 11259, National Bureau of Economic Research, Inc.
  7. Daniel Millimet & Rusty Tchernis, 2008. "On the Specification of Propensity Scores: with Applications to the Analysis of Trade Policies," Caepr Working Papers 2006-013_Updated, Center for Applied Economics and Policy Research, Economics Department, Indiana University Bloomington.
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