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

Cuong Nguyen Viet

MPRA Paper from University Library of Munich, Germany

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.

Keywords: Impact evaluation; treatment effect; propensity score matching; covariate selection; Monte Carlo (search for similar items in EconPapers)
JEL-codes: C14 C15 H43 (search for similar items in EconPapers)
Date: 2012-02-10
New Economics Papers: this item is included in nep-ecm
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:pra:mprapa:36377

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