Individuals’ pro-social behaviors are driven by altruistic and selfish motivations. In this paper we explore how the introduction of external incentives would influence one’s pro-social behavior both in the short term and in the long run. Using a large data set on Amazon product reviews, we design a quasiexperimental approach where we combine a propensity score matching (PSM) and a difference-i...

We employed the propensity score matching and estimated the causal effect of being certified organic crop producers on farm household income and its various components in the United States. Contrary to the standard assumption in economic analysis, certified organic farmers do not earn significantly higher household income than conventional farmers. Certified organic crop producers earn higher r...

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 ...