Model averaging in semiparametric estimation of treatment effects

Toru Kitagawa*, Chris Muris

*Corresponding author for this work

Research output: Contribution to journalArticle (Academic Journal)peer-review

10 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)271-289
Number of pages19
JournalJournal of Econometrics
Issue number1
Early online date2 Apr 2016
Publication statusPublished - Jul 2016


  • Limit experiment
  • Model averaging
  • Propensity score
  • Treatment effects


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