Selection of Control Variables in Propensity Score Matching: Evidence from a Simulation Study

Nguyen Viet, Cuong (2012): Selection of Control Variables in Propensity Score Matching: Evidence from a Simulation Study.

Abstract

Propensity score matching is a widely-used method to measure the effect of a treatment in social as well as health sciences. An important issue in propensity score matching is how to select conditioning variables in estimation of the propensity score. It is commonly mentioned that only variables which affect both program participation and outcomes are selected. Using Monte Carlo simulation, this paper shows that efficiency in estimation of the Average Treatment Effect on the Treated can be gained if all the available observed variables in the outcome equation are included in the estimation of the propensity score.

• Item Type: MPRA Paper
• Language: English
• Keywords: Impact evaluation, treatment effect, propensity score matching, covariate selection, Monte Carlo
• Subjects: C - Mathematical and Quantitative Methods > C1 - Econometric and Statistical Methods and Methodology: General > C14 - Semiparametric and Nonparametric Methods: General; H - Public Economics > H4 - Publicly Provided Goods > H43 - Project Evaluation ; Social Discount Rate; C - Mathematical and Quantitative Methods > C1 - Econometric and Statistical Methods and Methodology: General > C15 - Statistical Simulation Methods: General

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