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Home Publications Covariate selection and model averaging in semiparametric estimation of treatment effects

Covariate selection and model averaging in semiparametric estimation of treatment effects

Cemmap Working Paper CWP61/13

In the practice of program evaluation, choosing the covariates and the functional form of the propensity score is an important choice for estimating treatment effects. This paper proposes data-driven model selection and model averaging procedures that address this issue for the propensity score weighting estimation of the average treatment effects for treated (ATT). Building on the focussed information criterion (FIC), the proposed selection and 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 localisation parameters in the local asymptotic framework. In our Monte Carlo studies, the averaging estimator outperforms the post-covariate-selection estimator in terms of MSE, and shows a substantial reduction in MSE compared to conventional ATT estimators. We apply the procedures to evaluate the effect of the labour market program described in LaLonde (1986).

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Cemmap Working Paper CWP46/15
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).