The use of propensity score methods (Rosenbaum and Rubin, 1983) have become popular for estimating causal inferences in observational studies in medical research (Austin, 2008) and in the social sciences (Thoemmes and Kim, 2011). In most cases however, the use of propensity score methods have been confined to a single treatment. Several researchers have suggested using propensity score methods ...

background: the aim of this study was to investigate the relationship between congenital hypothyroidism and several variables using two different adjustment methods. methods: this matched case-control study was conducted in hamadan province, the west of iran, in 2012 enrolling neonates born between 2005 and 2011 and covered by screening program for congenital hypothyroidism. the neonates with t...

*corresponding author: [email protected] purpose: to compare the visual and anatomical outcomes and complications of two surgical methods in patients with pseudophakic / aphakic retinal detachment (pard) using propensity score analysis. methods: in this study, the propensity score matching was used as a new approach to control confounding factors. propensity score was calculated using the...

Because of the difficulty in multi-dimensional matching and missing variables, investigation of long-run stock performance after major corporate events has been plagued by the “bad model problem”. This study contributes to the literature by implementing the propensity score estimator which is able to match firms in multiple dimensions simultaneously, and illustrate this methodology in the conte...

This paper applies recently developed cross-sectional and longitudinal propensity score matching estimators to data from the National Supported Work Demonstration that have been previously analyzed by LaLonde (1986) and Dehejia and Wahba (1998,1999). We find little support for recent claims in the econometrics and statistics literatures that traditional, cross-sectional matching estimators gene...

Evidence-based management requires management scholars to draw causal inferences. Researchers generally rely on observational data sets and regression models where the independent variables have not been exogenously manipulated to estimate causal effects; however, using such models on observational data sets can produce a biased effect size of treatment intervention. This article introduces the...

When the treatment method was adjusted for propensity score in the propensity-score-matched pairs (n=100), we found that the hernia recurrence (32.0% vs 6.0%, p=0.002), overall complication (32.0% vs 6.0%, p=0.002), and freedom from hernia recurrence (68.2% vs 31.8%, p=0.001) rates were worse after bridged repair. We did not observe differences in wound healing and mesh complications between th...

In this paper, we give a short overview of some propensity score matching estimators suggested in the evaluation literature and we provide a set of Stata programs which we illustrate using the National Supported Work (NSW) demonstration widely known in labor economics.

Doubly robust estimation combines a form of outcome regression with a model for the exposure (i.e., the propensity score) to estimate the causal effect of an exposure on an outcome. When used individually to estimate a causal effect, both outcome regression and propensity score methods are unbiased only if the statistical model is correctly specified. The doubly robust estimator combines these ...

We explore the finite sample properties of several semiparametric estimators of average treatment effects, including propensity score reweighting, matching, double robust, and control function estimators. When there is good overlap in the distribution of propensity scores for treatment and control units, reweighting estimators are preferred on bias grounds and attain the semiparametric efficien...