Method: Propensity score matching was used to compare time to reconviction among 3,960 matched pairs of offenders, in which one of each pair received a prison sentence of 12 months or less and the other received a suspended sentence of two years or less. Kaplan Meier survival analysis was then used to examine time to the first proven offence committed after the index court appearance. Adjustmen...

The consistency of propensity score (PS) estimators relies on correct specification of the PS model. The PS is frequently estimated using main-effects logistic regression. However, the underlying model assumptions may not hold. Machine learning methods provide an alternative nonparametric approach to PS estimation. In this simulation study, we evaluated the benefit of using Super Learner (SL) f...

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...

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 ...

The propensity score plays a central role in a variety of causal inference settings. In particular, matching and weighting methods based on the estimated propensity score have become increasingly common in the analysis of observational data. Despite their popularity and theoretical appeal, the main practical difficulty of these methods is that the propensity score must be estimated. Researchers...

Nutritional labeling has been of much interest to policy makers and health advocates due to rising obesity trends. So can nutritional label use really help reduce body weight outcomes? This study evaluates the impact of nutritional label use on body weight using the propensity score matching technique. We conducted a series of tests related to variable choice of the propensity score specificati...