In recent years, propensity score matching (PSM) has gained attention as a potential method for estimating the impact of public policy programs in the absence of experimental evaluations. In this study, we evaluate the usefulness of PSM for estimating the impact of a program change in an educational context (Tennessee’s Student Teacher Achievement Ratio Project [Project STAR]). Because Tennesse...

The propensity score method is frequently used to deal with bias from standard regression in observational studies. The propensity score method involves calculating the conditional probability (propensity) of being in the treated group (of the exposure) given a set of covariates, weighting (or sampling) the data based on these propensity scores, and then analyzing the outcome using the weighted...

The federal Section 8 housing program provides eligible low-income families with an income-conditioned voucher that can be used to lease privately owned, affordable rental housing units. This paper extends prior research on the effectiveness of housing support programs in several ways. We use a quasi-experimental, propensity score matching research design, and examine the effect of housing vouc...

Using logistic regression models to predict the probability that a unit will respond is one method for adjusting for survey nonresponse. The inverse of the propensity score can be the weight adjustment factor. This method can make use of more predictive variables than in the weighting class method. Having used this method for two previous rounds of a large physician survey, this paper describes...

The problem of estimating average treatment effect is of fundamental importance when evaluating the effectiveness of medical treatments or social intervention policies. Most of the existing methods for estimating average treatment effect rely on some parametric assumptions onthe propensity score model or outcome regression model one way or the other. In reality, both models are prone to misspec...

Propensity scores are often used for stratification of treatment and control groups of subjects in observational data to remove confounding bias when estimating of causal effect of the treatment on an outcome in so-called potential outcome causal modeling framework. In this article, we try to get some insights into basic behavior of the propensity scores in a probabilistic sense. We do a simple...

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...