In this article, we apply a generalization of the propensity score of Rosenbaum and Rubin (1983b). Techniques based on the propensity score have long been used for causal inference in observational studies for reducing bias caused by non-random treatment assignment. In last years, Joffe and...

Regional and national development policies play an important role to support local enterprises in Italy. The amount of financial aid may be a key feature for firms’ employment policies. We study the impact on employment of the amount of financial aid attributed to enterprises located in...

In this article, we briefly review the role of the propensity score in estimating dose-response functions as described in Hirano and Imbens (2004, Applied Bayesian Modeling and Causal Inference from Incomplete-Data Perspectives, 73-84). Then we present a set of Stata programs that estimate the...

We employ quantile regression fixed effects models to estimate the income-pollution relationship on <italic>NO</italic> _{<italic>x</italic>} (nitrogen oxide) and <italic>SO</italic> ₂ (sulfur dioxide) using U.S. data. Conditional median results suggest that conditional mean methods provide too optimistic estimates about emissions reduction for...</italic>

We semiparametrically estimate average causal effects of different lengths of exposure to academic and vocational instruction in the Job Corps (JC) under the assumption that selection into different lengths is based on a rich set of observed covariates and time-invariant factors. We find that...

An important goal when analyzing the causal effect of a treatment on an outcome is to understand the mechanisms through which the treatment causally works. We define a causal mechanism effect of a treatment and the causal effect net of that mechanism using the potential outcomes framework. These...

An important goal in the analysis of the causal effect of a treatment on an outcome is to understand the mechanisms through which the treatment causally works. In the economics literature, however, there seems to be no available framework to estimate the relative importance of different causal...

We assess the effectiveness of Job Corps (JC), the largest job training program targeting disadvantaged youth in the United States, by constructing nonparametric bounds for the average and quantile treatment effects of the program on wages. Our preferred estimates point toward convincing...

We derive nonparametric bounds for local average treatment effects without requiring the exclusion restriction assumption to hold or an outcome with a bounded support. Instead, we employ assumptions requiring weak monotonicity of mean potential outcomes within or across subpopulations defined by...

When analyzing the causal e§ect of a treatment on an outcome it is important to un- derstand the mechanisms or channels through which the treatment works. In this paper we study net and mechanism average treatment e§ects (NATE and MATE, respectively), which provide an intuitive decomposition...