Funded by collage st. innovative projects Received: January 21, 2011 Accepted: February 10, 2011 doi:10.5539/jmr.v3n3p52 Abstract Causal inferences on the average treatment effect in observational studies are always difficult problems because the distributions of samples in the two treatment groups can not be observed at the same time, and the estimation of the treatment effect is often biased....

The popularity of matching techniques has increased considerably during the last decades. They are mainly used for matching treatment and control units in order to estimate causal treatment effects from observational studies or for integrating two or more data sets that share a common subset of covariates. In focusing on causal inference with observational studies, we discuss multivariate match...

Foundation and a grant from the Huntsman Center at the Wharton School. The opinions expressed are those of the authors and do not necessarily reflect the views of the research sponsors. The views expressed herein are those of the author(s) and not necessarily those of the National Bureau of Economic Research. ABSTRACT This paper examines the determinants of M&A activity in the pharmaceutical-bi...

Propensity score methods are an increasingly popular technique for causal inference. To estimate propensity scores, one must model the distribution of the treatment indicator given a vector of covariates. Much of work has been done in the case of covariates that are fully observed. Many studies, such as longitudinal surveys, suffer from missing covariate. In this paper, different approaches nam...

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...