The propensity score is the conditional probability of assignment to a particular treatment given a vector of observed covariates. Previous theoretical arguments have shown that subclassification on the propensity score will balance all observed covariates. Subclassification on an estimated propensity score is illustrated, using observational data on treatments for coronary artery disease. Five...

This paper re-examines the impact of a firm’s outward foreign direct investemnt on its R&D spending in the home country with propensity score matching method. Employing firm-level panel data on Taiwan’s manufacturing firms covering 1987-2003, this paper first demonstrates that firms with firm-specific and ownership advantages are more likely to undertake overseas inverstment. Controlling for ou...

Often it is infeasible or unethical to use random assignment in educational settings to study important constructs and questions. Hence, educational research often uses observational data, such as large-scale secondary data sets and state and school district data, and quasi-experimental designs. One method of reducing selection bias in estimations of treatment effects is propensity score analys...

Inverse probability of treatment weighting (IPTW) is a popular method for estimating causal effects in many disciplines. However, empirical studies show that the IPTW estimators can be sensitive to the misspecification of propensity score model. To address this problem, several researchers have proposed new methods to estimate propensity score by directly optimizing the balance of pre-treatment...

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...