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.

Propensity score based-estimators are commonly used to estimate causal effects in evaluation research. To reduce bias in observational studies researchers might be tempted to include many, perhaps correlated, covariates when estimating the propensity score model. Taking into account that the propensity score is estimated, this study investigates how the efficiency of matching, inverse probabili...

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 ...

Objectives. The transition to adulthood can be challenging, especially for children of incarcerated parents. Drawing on reentry and life-course scholarship, we argue that parental incarceration may adversely affect multiple life outcomes for children as they progress from adolescence into adulthood and that such effects may persist from early young adulthood into late young adulthood. Methods. ...

This paper assesses nonexperimental estimators using results from a six-state random assignment study of mandatory welfare-to-work programs. The assessment addresses two questions: which nonexperimental methods provide the most accurate estimates; and do the best methods work well enough to replace random assignment? Three tentative conclusions emerge. Nonexperimental bias was larger in the med...

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 propensity score to be correctly specified. The recently proposed covariate balancing propensity score (CBPS) methodology weakens this assumption by directly optimizing sample covariate b...