Many commonly used treatment effects estimators rely on the unconfoundedness assumption (“selection on observables") which is fundamentally non-testable. When evaluating the effects of labor market policies, researchers need to observe variables that affect both treatment participation and...

We consider the problem of using data from several programs, each implemented at a different location, to compare what their effect would be if they were implemented at a specific location. In particular, we study the effectiveness of nonexperimental strategies in adjusting for differences...

Many commonly used treatment effects estimators rely on the unconfoundedness assumption ("selection on observables") which is fundamentally non-testable. When evaluating the effects of labor market policies, researchers need to observe variables that affect both treatment participation and labor...

We study the effectiveness of nonexperimental strategies in adjusting for comparison group differences when using data from several programs, each implemented at a different location, to compare their effect if implemented at alternative locations. First, we adjust for individual characteristics...

This paper assesses whether a causal relationship exists between recent increases in female labor force participation and the increased prevalence of obesity amongst women. The expansions of the Earned Income Tax Credit (EITC) in the 1980s and 1990s have been established by prior literature as...

Estimation of average treatment effects under unconfounded or ignorable treatment assignment is often hampered by lack of overlap in the covariate distributions between treatment groups. This lack of overlap can lead to imprecise estimates, and can make commonly used estimators sensitive to the...

Estimation of average treatment effects under unconfoundedness or exogenous treatment assignment is often hampered by lack of overlap in the covariate distributions. This lack of overlap can lead to imprecise estimates and can make commonly used estimators sensitive to the choice of...

A large part of the recent literature on program evaluation has focused on estimation of the average effect of the treatment under assumptions of unconfoundedness or ignorability following the seminal work by Rubin (1974) and Rosenbaum and Rubin (1983). In many cases however, researchers are...

Estimation of average treatment effects under unconfounded or ignorable treatment assignment is often hampered by lack of overlap in the covariate distributions. This lack of overlap can lead to imprecise estimates and can make commonly used estimators sensitive to the choice of specification....