Model averaging in semiparametric estimation of treatment effects

Toru Kitagawa and Chris Muris

Journal of Econometrics, 2016, vol. 193, issue 1, 271-289

Abstract: Choosing the covariates and functional form of the propensity score is an important choice for estimating treatment effects. This paper proposes a data-driven way of averaging the estimators over candidate specifications to resolve the specification uncertainty in the propensity score weighting estimation of the ATT. The proposed procedures minimize the estimated MSE of the ATT estimator in a local asymptotic framework. We formulate model averaging as a statistical decision problem in a limit experiment, and derive an averaging scheme that is Bayes optimal with respect to a given prior. The averaging estimator outperforms selection estimators and the estimators in any of the candidate models in terms of Bayes asymptotic MSE. Our Monte Carlo studies illustrate the size of the MSE gains. We apply the averaging procedure to evaluate the effect of a labor market program.

Keywords: Treatment effects; Propensity score; Model averaging; Limit experiment (search for similar items in EconPapers)
JEL-codes: C13 C21 C52 (search for similar items in EconPapers)
Date: 2016
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Citations: View citations in EconPapers (14)

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Related works:
Working Paper: Model averaging in semiparametric estimation of treatment effects (2015)
Working Paper: Model averaging in semiparametric estimation of treatment effects (2015)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:econom:v:193:y:2016:i:1:p:271-289

DOI: 10.1016/j.jeconom.2016.03.002

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