In the online supplement we present details of additional simulations and results that have been referred to in the main text of the paper. In Appendix 1 we present results of the treatment effect estimates from the Across and Within methods in the simulations of Section 3 when matching is performed with replacement. In Appendix 2 we investigate different applications of propensity scores to es...

This paper evaluates the effects of a property tax bonus to promote the installation of solar–thermal energy systems in buildings in Andalusia (southern Spain). The propensity score matching methodology is used. The treatment group consists of municipalities of Andalusia that established property tax bonuses in their municipalities in 2010. The control group consists of municipalities that did ...

This article provides an assessment of the causal effect of customer relationship management (CRM) applications on one-to-one marketing effectiveness. We use a potential outcomes based propensity score approach to assess this causal effect. We find that firms using CRM systems have greater levels of one-to-one marketing effectiveness. We discuss the strengths and challenges of using the propens...

We congratulate Kang and Schafer (KS) on their excellent article comparing various estimators of a population mean in the presence of missing data, and thank the Editor for organizing the discussion. In this communication, we systematically examine the propensity score (PS) and the outcome regression (OR) approaches and doubly robust (DR) estimation, which are all discussed by KS. The aim is to...

Propensity score matching and weighting are popular methods when estimating causal effects in observational studies. Beyond the assumption of unconfoundedness, however, these methods also require the model for the propensity score to be correctly specified. The recently proposed covariate balancing propensity score (CBPS) methodology increases the robustness to model misspecification by directl...

Propensity score applications are often used to evaluate educational program impact. However, various options are available to estimate both propensity scores and construct comparison groups. This study used a student achievement dataset with commonly available covariates to compare different propensity scoring estimation methods (logistic regression, boosted regression, and Bayesian logistic r...

This study considers variance estimation when estimating the asymptotic variance of a propensity score matching estimator for the average treatment effect. We investigate the role of smoothing parameters in a variance estimator based on matching. We also study the properties of estimators using local linear estimation. Simulations demonstrate that large gains can be made in terms of mean square...

Using a simulation design that is based on empirical data, a recent study by Huber, Lechner and Wunsch (2013) finds that distance-weighted radius matching with bias adjustment as proposed in Lechner, Miquel and Wunsch (2011) is competitive among a broad range of propensity score-based estimators used to correct for mean differences due to observable covariates. In this companion paper, we furth...

In large observational studies there are often significant differences between characteristics of a treatment group and a no treatment group. Such differences should not exist in a randomized trial. These differences must be adjusted for in order to reduce treatment selection bias and determine treatment effect. There are several methods to reduce the bias of these differences and make the two ...