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Model averaging in semiparametric estimation of treatment effects

Authors: Toru Kitagawa and Chris Muris
Date: 13 August 2015
Type: cemmap Working Paper, CWP46/15
DOI: 10.1920/wp.cem.2015.4615

Abstract

In the practice of program evaluation, choosing the covariates and the functional form of the propensity score is an important choice that the researchers make when estimating treatment effects. This paper proposes a data-driven way of averaging the estimators over the candidate specifications in order to resolve the issue of specification uncertainty in the propensity score weighting estimation of the average treatment effects for treated (ATT). The proposed averaging procedures aim to minimize the estimated mean squared error (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 for the localization parameters. Analytical comparisons of the Bayes asymptotic MSE show that the averaging estimator outperforms post model selection estimators and the estimators in any of the candidate models. Our Monte Carlo studies confirm these theoretical results and illustrate the size of the MSE gains from averaging. We apply the averaging procedure to evaluate the effect of the labor market program analyzed in LaLonde (1986).

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Now published:
Toru Kitagawa and Chris Muris July 2016, Model averaging in semiparametric estimation of treatment effects, Journal Article, Elsevier
Previous version:
Toru Kitagawa and Chris Muris December 2013, Covariate selection and model averaging in semiparametric estimation of treatment effects, cemmap Working Paper, CWP61/13, Institute for Fiscal Studies

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