Minimizing Bias in Selection on Observables Estimators When Unconfoundness Fails

Daniel Millimet and Rusty Tchernis

No 3632, IZA Discussion Papers from Institute of Labor Economics (IZA)

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.

Keywords: selection on unobservables; unconfoundedness; treatment effects; propensity score; bias (search for similar items in EconPapers)
JEL-codes: C21 C52 (search for similar items in EconPapers)
Pages: 39 pages
Date: 2008-08
New Economics Papers: this item is included in nep-ecm
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Citations: View citations in EconPapers (2)

Published - 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

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Working Paper: Minimizing Bias in Selection on Observables Estimators When Unconfoundness Fails (2008)
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