Outliers can be particularly hard to detect, creating bias and inconsistency in the semi-parametric estimates. In this paper, we use Monte Carlo simulations to demonstrate that semi-parametric methods, such as matching, are biased in the presence of outliers. Bad and good leverage point outliers...

In defining selection bias, we have considered only the parallel universe of the treated group or the untreated group rather than including the parallel universe of the untreated group or the treated group. This makes causal inference theories unbalanced because they were developed on one side...

We partially identify population treatment effects in observational data under sample selection, without the benefit of random treatment assignment. We provide bounds both for the average and the quantile population treatment effects, combining assumptions for the selected and the non-selected...

In this paper, I assess the employment and income effect of divorce for women in West Germany between 2000 and 2005. With newly available administrative data that allows me to adopt a causal approach, I find strong negative employment effects with respect to marginal employment and strong...

Multidimensional heterogeneity and endogeneity are important features of models with multiple treatments. We consider a heterogeneous coefficients model where the outcome is a linear combination of dummy treatment variables, with each variable representing a different kind of treatment. We use...

Multidimensional heterogeneity and endogeneity are important features of models with multiple treatments. We consider a heterogeneous coefficients model where the outcome is a linear combination of dummy treatment variables, with each variable representing a different kind of treatment. We use...